Use of micro-ribonucleic acid (mirna) to diagnose transplant rejection and tolerance of immunosuppression therapy

ABSTRACT

The present invention relates to the discovery that the expression levels of some microRNAs (miRNAs) can use a diagnostic signature to predict transplant outcomes in a transplant recipient. Thus, in various embodiments described herein, the methods of the invention relate to methods of diagnosing a transplant subject for acute rejection such as acute cellular rejection (ACR), methods of predicting a subject&#39;s risk of having or developing ACR and methods of assessing in a subject the likelihood of a successful or failure minimization of immunosuppression therapy (IST) dosage from standard ranges.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalApplication No. 61/977,980, filed Apr. 10, 2014, which is incorporatedherein by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grants5U01A1063589-0551 and 5U01A1063589-05 and ITN00111-00sc used for theGradual Withdrawal of Immune System Suppressing Drugs in PatientsReceiving a Liver Transplant (AWISH) with a ClinicalTrials.govIdentifier: NCT00135694 awarded by National Institute of Allergy andInfectious Diseases (NIAID). The government has certain rights in theinvention.

BACKGROUND OF THE INVENTION

Solid organ transplantation provides life-saving therapy for patientswith end-stage organ disease. In 2010, a total of 28,664 transplantswere performed in the U.S., including 16,899 kidney, 6291 liver, 2333heart, and 1770 lung transplants (Engels et al., 2011, JAMA, 306(17):1891-1901). Although there has been significant improvements inimmunosuppressant therapies and in patient treatment pre- andpost-transplant surgery, rejection of graft still affects approximately60% of transplanted individuals and is thus still major risk factors ofgraft loss with rejection observed in up to 40% of transplantedindividuals within the first year post-transplant (Jain et at., 2000,Ann Surg. 232(4): 490-500). Acute rejection is also a known risk factorfor progressing to chronic rejection and thus detection and treatment ofacute rejection episodes as early as possible is a major goal tominimize graft damage and to stem downstream rejection episodes. In mostcases, adaptive immune responses to the grafted tissues are the majorimpediment to successful transplantation. Rejection is caused by immuneresponses to alloantigens on the graft, which are proteins that varyfrom individual to individual within a species and are thereforeperceived as foreign by the recipient. (Janeway, et al., 2001,Immunobiology: The Immune System in Health and Disease. 5th edition. NewYork: Garland Science).

Current monitoring and diagnostic tools are limited in their ability todiagnose acute rejection at early stages e.g. acute kidney allograftrejection is currently diagnosed following needle core biopsy of thegraft, a highly invasive procedure precipitated by crude biomarkers suchas an increase in the levels of creatinine in a recipients serum(Girlanda et al., 2007, Semin Nephrol. July; 27(4):462-78). Serumcreatinine levels lack the sensitivity and specificity required toeffectively predicting rejection yet it remains the best surrogatemarkers of acute rejection (Zhou et al., 2006, Nephrol Self AssessProgram. 5(2): 63-71). In liver transplantation, the presence ofelevated levels of aspartate transferase (AST) and alanine transferase(ALT) are used as an indicator to assess liver damage (Giboney, 2005, AmFam Physician 71(6): 1105-10). However, a level of more than three timesof normal may also be due to include alcohol toxicity, viral hepatitis,liver cancer, sepsis, Wilson disease, autoimmune hepatitis and drugtoxicity (Giboney, 2005, Am Fam Physician 15;71(6):1105-1110; Raurich etal., 2009, Hepatol. Res. 39 (7): 700-5.)

The development of a methodology to allow rapid diagnosis of earlyallograft rejection including but not limited to kidney, heart, lung,liver, pancreas, bone, bone marrow, bowel, nerve, stem cells,transplants derived from stem cells as well as tissue component andtissue composites, would be a major advancement in the field. Allograftbiopsies are currently highly invasive and are plagued by a number ofcomplications including bleeding of the site of puncture, shock,allograft fistulas, and even graft loss and the biopsy procedure carriesa greater risk in children. The availability of a non-invasivediagnostic test with high sensitivity and specificity to informclinicians regarding the status of the patient's rejection trajectorywould be of considerable value.

Over the last decade advances in surgical techniques, immunosuppressivetherapies and infectious monitoring and treatment have revolutionizedpatient and graft survival. However, despite this success, transplantrecipients still exhibit much higher morbidity and mortality than thegeneral population. Although this is in part due to the effects ofchronic allograft injury, the main causes are comorbidities influencedby chronic immunosuppressive drug usage (Soulillou and Giral 2007,Transplantation 72 (Suppl 12): S89-93; Hourmant et al., 1998, Lancet351: 623-628; Halloran, 2004, N Engl J Med 351: 2715-2729).

Immunosuppression related toxicities can be significant. For instance,several studies in adult liver transplant recipients, have shown atime-dependent continuous decline in renal function with exposure toimmunosuppressive therapy. Other important complications of long termimmunosuppression include new onset of diabetes after transplantation(NODAT), hypertension, hyperlipidemia and the need for statin therapy(Srinivas et al., 2008, CJASN: (Supplement 2) S101-S116). To redressthis situation, research priorities in organ transplantation are movingaway from the search of novel powerful immunosuppressive drugs towardthe identification of strategies to minimize immunosuppression.

Biomarkers can be used to determine the propensity to develop a disease,measure its progress, or predict prognosis (Wehling, 2006, Eur. J. Clin.Pharmacol, 62:91-95). In clinical trials, biomarkers can help in patientstratification and thereby increase the chances of a successful outcomeby targeting the appropriate population. In addition, biomarkers canpave the way to individualize treatment and thereby usher in a new erain personalized medicine (Frank et al., 2003, Nat. Rev. Drug Discov.2:566-580). Incorporation of molecular biomarkers into immunosuppressiontreatments can have large benefits such as the avoidance of invasivebiopsies as well as individualized guidance of minimization resulting inreductions in drug-related toxicities.

MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22nucleotides that regulate the posttranscriptional expression of targetgenes (Bartel, 2004, Cell 116: 281-297). The biogenesis of miRNA is amultistep process occurring in the cell nucleus and cytoplasm. Themature miRNA is incorporated into the RNA-induced silencing complex tobind the 3′ untranslated region (UTR) of mRNA, leading to mRNAdegradation or translational inhibition (Kim et al., 2006, Trends Genet22: 165-173). To date over 1000 human miRNAs have been identifiedalthough the target genes of many remain unknown (Bentwich et at., 2005,Nat Genet; 37: 766-770; Friedman et al., 2009, Genome Res 19: 92-105).miRNAs have been shown to play crucial roles in cellular development,cell differentiation, tumorigenesis, apoptosis and proliferation (He etal., 2004, Nat Rev Genet. 5: 522-531; Meltzer, 2005. Nature; 435:745-746; Chen et al., 2004, Science; 303: 83-86.). Further, miRNAs areinvolved in innate and adaptive immune responses (Harris et al., 2010,Am J Transplant; 10: 713-719; Lindsay, 2008, Trends Immunol; 29:343-351.). For example, miR-181a is an intrinsic modulator of T-cellsensitivity and selection that facilitates clonal deletion by modulatingthe T-cell receptor (TCR) signaling threshold of thymocytes (Li et al.,2007, Cell 129: 147-161; Ebert et al., 2009, Nat Immunol; 10:1162-1169.). miR-155 is important for cytokine production by T and Bcells and antigen presentation by dendritic cells (Rodriguez et al.,2007, Science; 316: 608-611.). Thus miRNAs as immune regulators maygovern expression of genes relevant to allograft rejection, toleranceinduction and posttransplant infection in recipients of organtransplants (Harris et al., 2010, Am J Transplant; 10:713-719). Recentstudies have demonstrated differential expression of miRNAs afterclinical renal transplantation. It was recently demonstrated that miRNAsare present in the serum and plasma of humans and other mammals, such asrats, mice, cows and horses (Chen et al., 2008, Cell Res. 18:997-1006;Mitchell et al., 2008, Proc. Natl. Acad. Sci. USA 105: 10513-10518).This finding opens up the feasibility of using miRNAs as biomarkers ofdisease. Further evidence for the presence of miRNAs in body fluids camefrom an analysis of urine samples (Gilad et al., 2008, PLoS One3:e3148). Four miRNAs were significantly elevated in urine fromurothelial bladder cancer patients, demonstrating the utility of miRNAsas a noninvasive diagnostic option (Hanke et al, 2009, Urol. Oncol). Allof these studies illustrate the potential use of miRNAs as novelbiomarkers amenable to clinical diagnosis in translational medicine(Gilad et al, 2008, PLoS One 3:e3148; Weber et al, 2010, Clin. Chem. 56:1733-1741; Etheridge et al, 2011, Mutat. Res.; Scholer et al, 2010, Exp.Hematol 38: 1126-1130).

There is a great need in the art for methods for detecting andquantifying miRNA expression for the diagnosis of transplant rejectionand tolerance of immunosuppression therapy in a patient. Furthermore,there is a need in the art for a non-invasive diagnostic test withstrong sensitivity and selectivity to inform clinicians regarding thestatus of the patient's rejection trajectory to provide the besttreatment modalities. The present invention satisfies these needs.

SUMMARY OF THE INVENTION

The invention includes a method for detecting or predicting transplantrejection of a transplanted organ in a subject. This method comprisesdetermining a level of at least one miRNA expression in a sample fromthe subject, comparing the level of at least one miRNA in the samplefrom the subject relative to a baseline level in a control wherein adifference in the level of the least one miRNA in the sample from thelevel of the at least one miRNA in the control is indicative of an acutetransplant rejection, and further wherein when acute transplantrejection is indicated, treatment for the rejection is recommended.

The invention also includes a method for predicting minimization ofimmunosuppression therapy (IST) in a transplant subject. This methodcomprises determining a level of at least one miRNA expression in asample from the subject, comparing the level of at least one miRNA inthe sample from the subject relative to a baseline level in a controlwherein a difference in the level of the least one miRNA in the samplefrom the level of the at least one miRNA in the control is indicative oflikelihood of success or failure of IST minimization, and furtherwherein when failure of IST minimization is indicated, treatment of thesubject is recommended.

The invention further includes a composition for detecting or predictingtransplant rejection of a transplanted organ in a subject comprising aplurality of miRNAs consisting of SEQ ID NOs: 1-23.

The invention further includes a composition for detecting or predictingtransplant rejection of a transplanted organ in a subject comprising aplurality of miRNAs consisting of SEQ ID NOs: 1-23 and 97-134.

The invention further includes kit comprising a plurality ofoligonucleotides that are configured to detect at least one miRNA fromselected from the group consisting of SEQ ID NOs: 1-23 and 97-134.

The invention further includes a composition for detecting or predictingthe ability, or non-ability, of minimizing IST dosage in a subjectpost-transplantation comprising a plurality of miRNAs consisting of SEQID NOs: 6-8, 22, 24-48.

The invention further includes kit comprising a plurality ofoligonucleotides that are configured to detect at least one miRNA fromthe group consisting of SEQ ID NOs: 6-8, 22, 24-48.

In some embodiments, the acute transplant rejection comprises acutecellular rejection (ACR). In some embodiments, the at least one miRNAfor detecting or predicting transplant rejection of a transplanted organin a subject is selected from the group consisting of SEQ ID NOs: 1-3.In other embodiments, the at least one miRNA for detecting or predictingtransplant rejection of a transplanted organ in a subject is selectedfrom the group consisting of SEQ ID NOs: 4-15. In further embodiments,the at least one miRNA for detecting or predicting transplant rejectionof a transplanted organ in a subject is selected from the groupconsisting of SEQ ID NOs: 16-23.

In yet further embodiments, the at least one miRNA for detecting orpredicting transplant rejection of a transplanted organ in a subject isselected from the group consisting of SEQ ID NOs: 1-23. In yet furtherembodiments, the at least one miRNA for detecting or predictingtransplant rejection of a transplanted organ in a subject is selectedfrom the group consisting of SEQ ID NOs: 1-23 and 97-134. In someembodiments, the at least one miRNA for predicting minimization ofimmunosuppression therapy (IST) in a transplant subject is selected fromthe group consisting of SEQ ID NOs: 24-26. In other embodiments, the atleast one miRNA for predicting minimization of immunosuppression therapy(IST) in a transplant subject is selected from the group consisting ofSEQ ID NOs: 6-8, 22, 27-48. In other embodiments, the at least one miRNAfor predicting minimization of immunosuppression therapy (IST) in atransplant subject is selected from the group consisting of SEQ ID NOs:6-8, 22, 24-48. In some embodiments, the minimization of IST is lowerthan the initial dosage by at least 75%. In certain embodiments, theminimization of IST is lower than the initial dosage by at least 25%, byat least 30%, by at least 35%, by at least 40%, by at least 45%, by atleast 50%, by at least 55%, by at least 60%, by at least 65%, by atleast 70%, by at least 75%, by at least 80%, by at least 85%, by atleast 90%, by at least 95%, or by at least 100%.

In some embodiments, the level of the at least one miRNA is higher thanthe level of the at least one miRNA in the control by at least 1 fold.In other embodiments, determining the level of the at least one miRNAemploys at least one technique selected from the group consisting ofreverse transcription, PCR, microarray, and Next Generation Sequencing.In further embodiments, the sample is at least one selected from thegroup consisting of urine, peripheral blood, serum, bile,bronchoalveolar lavage (BAL) fluid, pericardial fluid, gastrointestinalfluids, stool samples, biological fluid gathered from an anatomic areain proximity to an allograft, and biological fluid from an allograft. Infurther embodiments, the transplanted organ is at least one selectedfrom the group consisting of heart, liver, lung, kidney, an intestine,pancreas, pancreatic islet cells, eye, skin, and stem cells. In furtherembodiments, the comparison of level of miRNA expression is computed ina regression model to indicate a trajectory of acute rejection of thetransplanted organ. In some embodiments, the kit's oligonucleotides areconfigured to detect at least SEQ ID NOs: 1-3. In other embodiments, atleast one of kit's oligonucleotides is selected from the groupconsisting of SEQ ID NOs: 49-71 and 135-172. In other embodiments, thekit's oligonucleotides are configured to detect at least SEQ ID NOs:24-26. In yet other embodiments, at least one of the kit'soligonucleotides is selected from the group consisting of SEQ ID NOs:53-55, 70, 72-96. In some embodiments, the subject is a mammal. In otherembodiments, the mammal is a human.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, there are depicted in thedrawings certain embodiments of the invention. However, the invention isnot limited to the precise arrangements and instrumentalities of theembodiments depicted in the drawings.

FIG. 1 is a graph illustrating a receiver operating characteristic (ROC)plot that outlines the fraction of true positives out of the totalactual positives (TPR=true positive rate) versus the fraction of falsepositives out of the total actual negatives (FPR=false positive rate).This plot represents the 3-miRNA serum ACR diagnosis signature(hsa-miR-125b, hsa-miR-100 and hsa-miR-483).

FIG. 2 is a graph that depicts the LOESS smoothing (non-parametricregression methodology) plot of the composite scores of 3-miRNA ACRsignature (hsa-miR-125b, hsa-miR-100 and hsa-miR-483) up to the day ofbiopsy diagnosed rejection. The signature prediction of an ACR islabeled “Yes” and a non-ACR is labeled “No”.

FIG. 3 is a graph illustrating the composite scores (least squaremeans±standard deviation, SD) of 3-miRNA tolerant signature at variousdoses during immunosuppression minimization between those who failed 25%dose (label=“Yes”) and those who were tolerant at 25% dose (label=“No”).

FIG. 4 is a graph representing a receiver operating characteristic (ROC)plot that outlines the fraction of true positives out of the totalactual positives (TPR=true positive rate) versus the fraction of falsepositives out of the total actual negatives (FPR=false positive rate).This plot illustrates the 3-miRNA serum ACR diagnosis signature found inthe replication dataset.

FIG. 5 is a series of box plots depicting the serum expression levels ofthe top five ACR-associated miRNAs identified by Qiagen Arrays. TheY-axis indicates miRNA expression levels (−dCt) and the X-axis indicatesACR status.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to the discovery that the expressionlevels of some microRNAs (miRNAs) can be used as diagnostic signature topredict transplant outcomes in a transplant recipient. Thus, in variousembodiments described herein, the methods of the invention relate tomethods of diagnosing a transplant subject for acute rejection such asacute cellular rejection (ACR), methods of predicting a subject's riskof having or developing ACR and methods of assessing if a subject isprone to a successful or failure reduction the immunosuppression therapy(IST) dosage from standard ranges.

Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, the preferred methodsand materials are described.

As used herein, each of the following terms has the meaning associatedwith it in this section.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

“About” as used herein when referring to a measurable value such as anamount, a temporal duration, and the like, is meant to encompassvariations of ±20% or ±10%, more preferably ±5%, even more preferably±1%, and still more preferably ±0, 1% from the specified value, as suchvariations are appropriate to perform the disclosed methods.

The term “abnormal” when used in the context of organisms, tissues,cells or components thereof, refers to those organisms, tissues, cellsor components thereof that differ in at least one observable ordetectable characteristic (e.g., age, treatment, time of day, etc.) fromthose organisms, tissues, cells or components thereof that display the“normal” (expected) respective characteristic. Characteristics which arenormal or expected for one cell or tissue type, might be abnormal for adifferent cell or tissue type.

A “disease” is a state of health of an animal wherein the animal cannotmaintain homeostasis, and wherein if the disease is not ameliorated thenthe animal's health continues to deteriorate.

In contrast, a “disorder” in an animal is a state of health in which theanimal is able to maintain homeostasis, but in which the animal's stateof health is less favorable than it would be in the absence of thedisorder. Left untreated, a disorder does not necessarily cause afurther decrease in the animal's state of health.

The terms “dysregulated” and “dysregulation” as used herein describes adecreased (down-regulated) or increased (up-regulated) level ofexpression of a miRNA present and detected in a sample obtained fromsubject as compared to the level of expression of that miRNA present ina control sample, such as a control sample obtained from one or morenormal, not-at-risk subjects, or from the same subject at a differenttime point. In some instances, the level of miRNA expression is comparedwith an average value obtained from more than one not-at-riskindividuals. In other instances, the level of miRNA expression iscompared with a miRNA level assessed in a sample obtained from onenormal, not-at-risk subject.

“Differentially increased expression” or “up regulation” refers toexpression levels which are at least 10% or more, for example, 20%, 30%,40%, or 50%, 60%, 70%, 80%, 90% higher or more, and/or 1.1 fold, 1.2fold, 1.4 fold, 1.6 fold, 1.8 fold, 2.0 fold higher or more, and any andall whole or partial increments therebetween, than a control.

“Differentially decreased expression” or “down regulation” refers toexpression levels which are at least 10% or more, for example, 20%, 30%,40%, or 50%, 60%, 70%, 80%, 90% lower or less, and/or 2.0 fold, 1.8fold, 1.6 fold, 1.4 fold, 1.2 fold, 1.1 fold or less lower, and any andall whole or partial increments therebetween, than a control.

The term “expression” as used herein is defined as the transcriptionand/or translation of a particular nucleotide sequence.

As used herein, “isolated” means altered or removed from the naturalstate through the actions, directly or indirectly, of a human being. Forexample, a nucleic acid or a peptide naturally present in a livinganimal is not “isolated,” but the same nucleic acid or peptide partiallyor completely separated from the coexisting materials of its naturalstate is “isolated.” An isolated nucleic acid or protein can exist insubstantially purified form, or can exist in a non-native environmentsuch as, for example, a host cell.

As used herein, “microRNA” or “miRNA” describes miRNA molecules,generally about 15 to about 50 nucleotides in length, preferably 17-23nucleotides, which can play a role in regulating gene expressionthrough, for example, a process termed RNA interference (RNAi). RNAidescribes a phenomenon whereby the presence of an RNA sequence that iscomplementary or antisense to a sequence in a target gene messenger RNA(mRNA) results in inhibition of expression of the target gene. miRNAsare processed from hairpin precursors of about 70 or more nucleotides(pre-miRNA) which are derived from primary transcripts (pri-miRNA)through sequential cleavage by RNAse III enzymes.

By “nucleic acid” is meant any nucleic acid, whether composed ofdeoxyribonucleosides or ribonucleosides, and whether composed ofphosphodiester linkages or modified linkages such as phosphotriester,phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate,carbamate, thioether, bridged phosphoramidate, bridged methylenephosphonate, phosphorothioate, methylphosphonate, phosphorodithioate,bridged phosphorothioate or sulfone linkages, and combinations of suchlinkages. The term nucleic acid also specifically includes nucleic acidscomposed of bases other than the five biologically occurring bases(adenine, guanine, thymine, cytosine and uracil).

The term, “polynucleotide” includes cDNA, RNA, DNA/RNA hybrid,anti-sense RNA, siRNA, miRNA, snoRNA, genomic DNA, synthetic forms, andmixed polymers, both sense and antisense strands, and may be chemicallyor biochemically modified to contain non-natural or derivatized,synthetic, or semisynthetic nucleotide bases. Also, included within thescope of the invention are alterations of a wild type or synthetic gene,including but not limited to deletion, insertion, substitution of one ormore nucleotides, or fusion to other polynucleotide sequences.

Conventional notation is used herein to describe polynucleotidesequences: the left-hand end of a single-stranded polynucleotidesequence is the 5′-end; the left-hand direction of a double-strandedpolynucleotide sequence is referred to as the 5′-direction.

The term “oligonucleotide” typically refers to short polynucleotides,generally no greater than about 60 nucleotides. It will be understoodthat when a nucleotide sequence is represented by a DNA sequence (i.e.,A, T, G, C), this also includes an RNA sequence (i.e., A, U, G, C) inwhich “U” replaces “T.”

The term “spiked-in” refers to a defined sequence nucleic acid species(such as a RNA species, sequence or transcript) that is added to asample during processing and used to assess the performance of amicroarray. “Spiked-in” refers to artificial sequences that can includestandard or modified nucleotides such as locked nucleic acids (LNAs),peptide nucleic acids (PNA), or nucleic acid analogues (e.g., isoG,isoC, etc.). In some embodiments, the defined sequence nucleic acidcomprises a sequence that is not likely to be found in the biologicalsample to be analyzed and is selected to have minimal self-hybridizationand cross hybridization with other similar sequences in the set. Suchspiked-in controls can be used to monitor microarray quality, in termsof dynamic range, reproducibility, etc. Different spiked-in controls canbe used to monitor different processes in a microarray analysis. In someembodiments, the measured degree of hybridization between the spiked-inand the control probes is used to calibrate and normalize thehybridization measurements of the sample RNA or miRNA.

As used herein, “hybridization,” “hybridize (s)” or “capable ofhybridizing” is understood to mean the forming of a double or triplestranded molecule or a molecule with partial double or triple strandednature. Complementary sequences in the nucleic acids pair with eachother to form a double helix. The resulting double-stranded nucleic acidis a “hybrid.” Hybridization may be between, for example twocomplementary or partially complementary sequences. The hybrid may havedouble-stranded regions and single stranded regions. The hybrid may be,for example, DNA:DNA, RNA:DNA or DNA:RNA. Hybrids may also be formedbetween modified nucleic acids (e.g., LNA compounds). One or both of thenucleic acids may be immobilized on a solid support. Hybridizationtechniques may be used to detect and isolate specific sequences, measurehomology, or define other characteristics of one or both strands. Thestability of a hybrid depends on a variety of factors including thelength of complementarity, the presence of mismatches within thecomplementary region, the temperature and the concentration of salt inthe reaction or nucleotide modifications in one of the two strands ofthe hybrid.

A “nucleic acid probe,” or a “probe”, as used herein, is a DNA probe oran RNA probe.

The term “Next-generation sequencing” (NGS), also known ashigh-throughput sequencing, is used herein to describe a number ofdifferent modern sequencing technologies that allow to sequence DNA andRNA much more quickly and cheaply than the previously used Sangersequencing (Metzker, 2010, Nature Reviews Genetics 11.1: 31-46). It isbased on micro- and nanotechnologies to reduce the size of sample, thereagent costs, and to enable massively parallel sequencing reactions. Itcan be highly multiplexed which allows simultaneous sequencing andanalysis of millions of samples. NGS includes first, second, third aswell as subsequent Next Generations Sequencing technologies.

“Sample” or “biological sample” as used herein means a biologicalmaterial from a subject, including but is not limited to organ, tissue,exosome, blood, plasma, saliva, urine and other body fluid. A sample canbe any source of material obtained from a subject.

The terms “subject” “patient,” “individual,” and the like are usedinterchangeably herein, and refer to any animal, or cells thereofwhether in vitro or in situ, amenable to the methods described herein.In certain non-limiting embodiments, the patient, subject or individualis a human. Non-human mammals include, for example, livestock and pets,such as ovine, bovine, porcine, canine, feline and murine mammals.Preferably, the subject is human. The term “subject” does not denote aparticular age or sex. Preferably the subject is a human patient. Insome embodiments, the subject is a human having received an organtransplant.

Ranges: throughout this disclosure, various aspects of the invention canbe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 2,7, 3, 4, 5, 5.3, and 6. Thisapplies regardless of the breadth of the range.

A “mutation” as used therein is a change in a DNA sequence resulting inan alteration from its natural state. The mutation can comprise deletionand/or insertion and/or duplication and/or substitution of at least onedeoxyribonucleic acid base such as a purine (adenine and/or thymine)and/or a pyrimidine (guanine and/or cytosine) Mutations may or may notproduce discernible changes in the observable characteristics(phenotype) of an organism (subject).

The term “biopsy” refers to a specimen obtained by removing tissue fromliving patients for diagnostic examination. The term includes aspirationbiopsies, brush biopsies, chorionic villus biopsies, endoscopicbiopsies, excision biopsies, needle biopsies (specimens obtained byremoval by aspiration through an appropriate needle or trocar thatpierces the skin, or the external surface of an organ, and into theunderlying tissue to be examined), open biopsies, punch biopsies(trephine), shave biopsies, sponge biopsies, and wedge biopsies.Biopsies also include a fine needle aspiration biopsy, a minicore needlebiopsy, and/or a conventional percutaneous core needle biopsy.

“Baseline expression” or “Baseline level of gene expression level”includes the particular gene expression level of a healthy subject or asubject with a well-functioning transplant. The baseline level of geneexpression includes the gene expression level of a subject without acuterejection. The baseline level of gene expression can be a number onpaper or the baseline level of gene expression from a control sample ofa healthy subject or a subject with a well-functioning transplant.

“Acute rejection” or “acute cellular rejection” refers to an immunereaction evoked by allografted organs. In general, the acute rejectionhas its onset 2-60 days after transplantation, and possibly othercell-specific antigens expressed by the tubular epithelium and vascularendothelium. It is caused by mismatched HLA antigens, and possibly othercell-specific antigens expressed by the tubular epithelium and vascularendothelium. It is believed that both delayed hypersensitivity andcytotoxicity mechanisms are involved. Acute rejection is characterizedby infiltration of the transplanted tissue by immune cells of therecipient, which carry out their effector function and destroy thetransplanted tissue. It can be characterized by interstitial vascularendothelial cell swelling, interstitial accumulation of lymphocytes,plasma cells, immunoblasts, macrophages, neutrophils; tubular separationwith edema/necrosis of tubular epithelium; swelling and vacuolization ofthe endothelial cells, vascular edema, bleeding and inflammation, renaltubular necrosis, sclerosed glomeruli, tubular ‘thyroidization’.

“Chronic rejection” generally occurs within several months to yearsafter engraftment, even in the presence of successful immunosuppressionof acute rejection. Fibrosis is a common factor in chronic rejection ofall types of organ transplants. Chronic rejection can typically bedescribed by a range of specific disorders that are characteristic ofthe particular organ. For example, in lung transplants, such disordersinclude fibroproliferative destruction of the airway (bronchiolitisobliterans); in heart transplants or transplants of cardiac tissue, suchas valve replacements, such disorders include fibrotic atherosclerosis;in kidney transplants, such disorders include, obstructive nephropathy,nephrosclerorsis, tubulointerstitial nephropathy; and in livertransplants, such disorders include disappearing bile duct syndrome.Chronic rejection can also be characterized by ischemic insult,denervation of the transplanted tissue, hyperlipidemia and hypertensionassociated with immunosuppressive drugs.

The term “transplant rejection” encompasses both acute and chronictransplant rejection.

The term “diagnosis” is used herein to refer to the identification orclassification of a molecular or pathological state, disease orcondition. For example, “diagnosis” may refer to identification of aparticular type of acute rejection, e.g., acute cellular rejection.

The term “prediction” is used herein to refer to the likelihood that apatient will develop acute rejection. Thus, prediction also includes thetime period without acute rejection.

As used herein, the term “transplantation” refers to the process oftaking a cell, tissue, or organ, called a “transplant” or “graft” fromone individual and placing it or them into a (usually) differentindividual. The individual who provides the transplant is called the“donor” and the individual who received the transplant is called the“host” (or “recipient”). An organ, or graft, transplanted between twogenetically different individuals of the same species is called an“allograft”. A graft transplanted between individuals of differentspecies is called a “xenograft”.

As used herein, “transplant rejection” refers to a functional andstructural deterioration of the organ due to an active immune responseexpressed by the recipient, and independent of non-immunologic causes oforgan dysfunction. Acute transplant rejection can result from theactivation of recipient's T cells and/or B cells; the rejectionprimarily due to T cells is classified as T cell mediated acuterejection or acute cellular rejection (ACR) and the rejection in which Bcells are primarily responsible is classified as antibody mediated acuterejection (AMR). In some embodiments, the methods and compositionsprovided can detect and/or predict acute cellular rejection.

As used herein, the terms “immunosuppression” or “immunosuppressivetherapy (IST)” involve an act that reduces the activation or efficacy ofthe immune system. Deliberately induced immunosuppression is performedto prevent the body from rejecting an organ transplant, treatinggraft-versus-host disease after a bone marrow transplant, or for thetreatment of auto-immune diseases such as rheumatoid arthritis orCrohn's disease.

As used herein, the term “tolerance” is a state of immuneunresponsiveness specific to a particular antigen or set of antigensinduced by previous exposure to that antigen or set. Tolerance isgenerally accepted to be an active process and, in essence, a learningexperience for T cells. Tolerance, as used herein, refers to theinhibition of a graft recipient's ability to mount an immune responsewhich would otherwise occur, e.g., in response to the introduction of anonself MHC antigen into the recipient. Tolerance can involve humoral,cellular, or both humoral and cellular responses.

As used herein, the term “biomarker” includes a polynucleotide orpolypeptide molecule which is present or increased in quantity oractivity in subjects having acute rejection or where the acute rejectionis anticipated.

As used herein, the term “biomarkers for diagnosis” or “diagnosissignature” includes a group of markers such as miRNA, the quantity oractivity of each member of which is correlated with subjects havingacute rejection or where the acute rejection is anticipated. In certainembodiments, the diagnosis signature may include only those markers. Insome embodiments, the signature includes one, two, three, four, five,six, seven, eight, or nine or more miRNAs.

As used herein, the term “biomarkers for tolerance” or “tolerancesignature” includes a group of markers such as miRNA, the quantity oractivity of each member of which is correlated with subjects havingtolerance for a certain level of immunosuppression minimization or wherethe immunosuppression minimization is anticipated. In certainembodiments, the tolerance signature may include only those markers. Insome embodiments, the signature includes one, two, three, four, five,six, seven, eight, or nine or more miRNAs.

Methods of the Invention

The invention relates to the unexpected discovery that it is possible toanticipate the future development of acute cellular rejection with ahigh degree of accuracy; and diagnose acute cellular rejection with ahigh degree of sensitivity and specificity without performing atransplant biopsy, by measuring the levels certain microRNAs, referredas “ACR diagnosis signature”, in serum samples from liver transplantrecipients. Furthermore, by measuring the level of other microRNAscandidates, referred as “IST tolerance signature”, the invention enablesthe prediction in a transplant subject of the success or failure ofminimizing immunosuppression therapy (IST) dosage from standard ranges.

In some embodiments, miRNAs associated with ACR are differentiallyexpressed. In yet other embodiments, miRNAs associated with failure inminimizing IST are differentially expressed. Thus, the invention relatesto compositions and methods useful for the detection and quantificationof miRNAs and the use of these miRNAs signature for the diagnosis,assessment, and characterization of trajectory of-, andtransplant-outcomes, as well as the adjustment of IST dosage in asubject in need thereof.

Reference Amount of Expression of the miRNA Marker(s)

The method of the invention includes comparing a measured amount ofexpression of a miRNA marker(s) in a biological sample from a subject toa reference amount (i.e. the control) of expression of a miRNAmarker(s).

Reference for Detecting or Predicting Acute Cellular Rejection

In one embodiment, the reference (i.e. the control) level of expressionof the miRNA(s) may be obtained by measuring the expression level of amiRNA in a subject having a non-rejected organ. For example, the subjecthaving a non-rejected organ may include a healthy subject. Preferably,the healthy subject is a subject of similar age, gender, race,graft-donor source, Banff histologic grade, and/or that underwent thesame initial anti-rejection treatment as the patient having atransplanted organ for which risk of organ failure is to assessed.

Another example of a subject having a non-rejected organ is a subjecthaving a well-functioning transplanted organ. A well-functioning (e.g.,stable) transplanted organ is defined as a transplanted organ that doesnot exhibit organ failure (e.g., rejection). Preferably, awell-functioning transplanted organ is a transplanted organ that has notdeveloped transplant dysfunction or morphologic evidence of transplantinjury in areas of the transplant. Preferably, the subject having awell-functioning (e.g., stable) transplanted organ is a subject ofsimilar age, gender, race, graft-donor source, Banff histologic grade,and/or that underwent the same initial anti-rejection treatment as thesubject having a transplanted organ for which risk of organ failure isto be assessed.

In another embodiment, the reference amount is obtained by measuring anamount of expression of the miRNA in a second biological sample from thesubject. For example, the second biological sample may be obtained fromthe subject before the organ transplantation and/or from anothernon-rejected organ of the subject.

In yet another embodiment, the reference amount of expression of themiRNA is a value for expression of the miRNA that is accepted in the art(e.g., spiked-in).

Reference for Predicting Success or Failure of MinimizingImmunosuppression Therapy (IST).

In one embodiment, the reference amount of expression of the miRNA isobtained by measuring an amount of expression of the miRNA in atransplant subject having a successful tolerance for a decrease in theIST dosage. For example, the subject under a lower IST dosage includes ahealthy subject. Preferably, the healthy subject is a subject of similarage, gender, race, graft-donor source, Banff histologic grade, and/orthat underwent the same initial anti-rejection treatment as the subjecthaving a transplanted organ for which the minimization of IST is toassessed.

Another example of a subject having a non-rejected organ is a subjecthaving a well-functioning transplanted organ. A well-functioning (e.g.,stable) transplanted organ may be defined as a transplanted organ thatdoes not exhibit organ failure (e.g., rejection). Preferably, awell-functioning transplanted organ is a transplanted organ that has notdeveloped transplant dysfunction or morphologic evidence of transplantinjury in areas of the transplant. Preferably, the subject having awell-functioning (e.g., stable) transplanted organ is a subject ofsimilar age, gender, race, graft-donor source, Banff histologic grade,and/or that underwent the same initial anti-rejection treatment as thesubject having a transplanted organ for which risk of organ failure isto assessed.

In another embodiment, the reference amount is obtained by measuring anamount of expression of the miRNA in a second biological sample from thesubject. For example, the second biological sample may be obtained fromthe subject before the organ transplantation and/or from anothernon-rejected organ of the subject.

In another embodiment, the reference amount is obtained by measuring anamount of expression of said miRNA in a second biological sample fromthe subject prior the organ transplantation and/or prior beginning ISTtreatment and/or prior beginning minimizing IST.

In yet another embodiment, the reference amount of expression of themiRNA is a value for expression of the miRNA that is accepted in the art(e.g., spiked-in).

Comparison of the Measured Amount of Expression of the miRNA Marker

The method includes comparing the measured amount of expression of themiRNA to the reference amount of expression of the miRNA.

For Detecting or Predicting Acute Cellular Rejection

The miRNA marker may be, for example, a miRNA selected fromhsa-miR-125b-5p, hsa-miR-100-5p, hsa-miR-483-5p, hsa-miR-885-5p,hsa-miR-122-5p, hsa-miR-99a-5p, hsa-miR-30a-5p, hsa-miR-497-5p,hsa-miR-194-5p, hsa-miR-34a-5p, hsa-miR-192-5p, hsa-miR-215,hsa-miR-375, hsa-miR-193a-5p, hsa-miR-483-5p, hsa-miR-505-3p,hsa-miR-378a-3p, hsa-miR-193b-3p, hsa-miR-874, hsa-miR-365a-3p,hsa-miR-152, hsa-miR-148a-3p and hsa-miR-29a-5p, or any combinationthereof.

In one embodiment, the miRNA marker is selected from hsa-miR-125b-5p,hsa-miR-100-5p, hsa-miR-483-5p, hsa-miR-885-5p, hsa-miR-122-5p,hsa-miR-99a-5p, hsa-miR-30a-5p, hsa-miR-497-5p, hsa-miR-194-5p,hsa-miR-34a-5p, hsa-miR-192-5p, hsa-miR-215, hsa-miR-375,hsa-miR-193a-5p and hsa-miR-483-5p, or any combination thereof.

In another embodiment, the miRNA marker is hsa-miR-125b-5p,hsa-miR-100-5p and hsa-miR-483-5p, wherein an increase of expression ofthe miRNA marker that is equivalent to at least about 1-fold as comparedto the reference amount of expression of the miRNA marker indicates anincreased risk of rejection of the transplanted organ.

An increase of expression that is equivalent to at least about 1-foldmay be an increase in an amount equivalent to at least about 1-, 2-, 3-,4-, 5-, 6-, 7-, 8-, 9-, 10-, 11-, 12-, 13-, 14-, 15-, 16-, 17-, 18-,19-, 20-fold, or more and any and all partial integers therebetween, ascompared with the increase in the reference amount of expression of themiRNA marker. Examples of methods to quantify an increase of expressionare known in the art, as are described in the Examples disclosedelsewhere herein.

For Predicting Success or Failure of Minimizing ImmunosuppressionTherapy (IST).

The miRNA marker may be, for example, a miRNA selected fromhsa-miR-146b-5p, hsa-miR-424-3p, hsa-miR-125a-5p, hsa-miR-342-3p,hsa-miR-150-5p, hsa-miR-421, hsa-miR-148a-3p, hsa-miR-223-5p,hsa-miR-495-3p, hsa-miR-497-5p, hsa-miR-29a-3p, hsa-miR-30a-5p,hsa-miR-374b-5p, hsa-let-7g-5p, hsa-miR-99a-5p, hsa-miR-18b-5p,hsa-miR-7-1-3p, hsa-miR-181c-5p, hsa-miR-454-3p, hsa-miR-485-3p,hsa-miR-374a-5p, hsa-miR-99b-5p, hsa-miR-192-5p, hsa-miR-191-5p,hsa-miR-21-5p, hsa-miR-24-3p, hsa-miR-27b-3p, hsa-miR-222-3p,hsa-miR-20a-3p and hsa-miR-106b-5p, or any combination thereof.

In one embodiment, the miRNA marker is hsa-miR-146b-5p, hsa-miR-424-3pand hsa-miR-125a-5p, wherein an increase of expression of the miRNAmarker that is equivalent to at least about 1-fold as compared to thereference amount of expression of the miRNA marker indicates anincreased risk of failing minimization of IST.

An increase of expression that is equivalent to at least about 1-foldmay be an increase in an amount equivalent to at least about 1-, 2-, 3-,4-, 5-, 6-, 7-, 8-, 9-, 10-, 11-, 12-, 13-, 14-, 15-, 16-, 17-, 18-,19-, 20-fold, or more, and any and all partial integers therebetween, ascompared with the increase in the reference amount of expression of themiRNA marker. Preferably, the increase is a fold value. Examples ofmethods to quantify an increase of expression are known in the art, asare described in the Examples disclosed elsewhere herein.

Normalization

In one embodiment, the invention includes normalizing the amount ofexpression of the miRNA marker. The method includes measuring an amountof expression of commercially available spiked-in markers as referencesagainst the expression level of miRNA from the subject.

For Detecting or Predicting Acute Cellular Rejection

The method further includes measuring an amount of expression of a miRNAmarker in a biological sample from a first subject having a rejectedorgan or at risk for rejecting an organ. The method further includesmeasuring an amount of expression of miRNA marker in a biological samplefrom a second subject having a non-rejected organ. In addition, themethod includes comparing the measured amount of the miRNA markersbetween these two types of subjects. Furthermore, when acute transplantrejection is indicated, treatment for the rejection is recommended.

When the level of miRNAs in the first subject is greater than the levelof miRNAs in second subject by an amount equivalent to at least 1-fold,the calculation indicates an increased risk of rejection of thetransplanted organ in the subject having a transplanted organ. Thecalculated increase that is at least 1-fold may be an increase that isequivalent to at least about 1-, 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, 10-,11-, 12-, 13-, 14-, 15-, 16-, 17-, 18-, 19-, 20-fold, or more and anyand all partial integers therebetween.

When the level of miRNAs in the first subject is greater than the levelof miRNAs in second subject by an amount equivalent to less than 1-fold,the calculation indicates an increased risk of rejection of thetransplanted organ in the subject having a transplanted organ. Thecalculated increase that is less than 1-fold may be an increase that isequivalent to at most about 0.9-, 0.8-, 0.7-, 0.6-, 0.5-, 0.4-, 0.3-,0.2-, or 0.1-fold, or less.

For Predicting Success or Failure of Minimizing ImmunosuppressionTherapy (IST).

The method further includes measuring an amount of expression of anendogenously expressed small non-coding reference RNA in a biologicalsample from a first tested subject under consideration for minimizationof IST dosage. The method further includes measuring an amount ofexpression of miRNA marker in a biological sample from a second subjecthaving a successful minimization of IST dosage. In addition, the methodincludes comparing the measured amount of the miRNA marker between thesetwo types of subjects. Furthermore, when failure of IST minimization isindicated, treatment of the subject is recommended.

When the level of miRNAs in the first subject is greater than the levelof miRNAs in second subject by an amount equivalent to at least 1-fold,the calculation indicates an increased risk of failing minimization ofIST dosage in a subject under IST treatment. The calculated increasethat is at least 1-fold may be an increase that is equivalent to atleast about 1-, 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, 10-, 11-, 12-, 13-, 14-,15-, 16-, 17-, 18-, 19-, 20-fold, or more and any and all partialintegers therebetween.

When the level of miRNAs in the first subject is greater than the levelof miRNAs in second subject by an amount equivalent to less than 1-fold,the calculation indicates an increased risk of failing minimization ofIST dosage in a subject under IST treatment. The calculated increasethat is less than 1-fold may be an increase that is equivalent to atmost about 0.9-, 0.8-, 0.7-, 0.6-, 0.5-, 0.4-, 0.3-, 0.2-, or 0.1-fold,or less.

Accordingly, in the present embodiment, fold changes or equivalentsthereof for the miRNA marker are normalized to the spiked-in referencemiRNAs.

Detecting Acute Transplant Rejection

In one embodiment, the invention includes a method of detecting acuterejection in a subject having received an organ transplant. The methodcomprises the steps of detecting a level of expression of miRNAindicative of acute rejection in a test sample from the subject, whereinthe miRNA is at least one selected from hsa-miR-125b-5p, hsa-miR-100-5p,hsa-miR-483-5p, hsa-miR-885-5p, hsa-miR-122-5p, hsa-miR-99a-5p,hsa-miR-30a-5p, hsa-miR-497-5p, hsa-miR-194-5p, hsa-miR-34a-5p,hsa-miR-192-5p, hsa-miR-215, hsa-miR-375, hsa-miR-193a-5p,hsa-miR-483-5p, hsa-miR-505-3p, hsa-miR-378a-3p, hsa-miR-193b-3p,hsa-miR-874, hsa-miR-365a-3p, hsa-miR-152, hsa-miR-148a-3p andhsa-miR-29a-5p or any combination thereof. Then comparing the level ofexpression of the miRNA in the test sample to the level of miRNA in acontrol sample, wherein an increase between the amount of the miRNA inthe test sample relative to the control sample indicates that thesubject has acute cellular rejection (ACR). Furthermore, when acutetransplant rejection is indicated, treatment for the rejection isrecommended.

The invention is based, in part, on the observation that increasedexpression of certain miRNAs comprising hsa-miR-125b-5p, hsa-miR-100-5p,hsa-miR-483-5p, hsa-miR-885-5p, hsa-miR-122-5p, hsa-miR-99a-5p,hsa-miR-30a-5p, hsa-miR-497-5p, hsa-miR-194-5p, hsa-miR-34a-5p,hsa-miR-192-5p, hsa-miR-215, hsa-miR-375, hsa-miR-193a-5p,hsa-miR-483-5p, hsa-miR-505-3p, hsa-miR-378a-3p, hsa-miR-193b-3p,hsa-miR-874, hsa-miR-365a-3p, hsa-miR-152, hsa-miR-148a-3p andhsa-miR-29a-5p, the miRNAs provided in Table 1, hsa-miR-4790-5p,hsa-miR-3692-3p, hsa-miR-4433b-3p, hsa-miR-6500-3p, hsa-miR-4445-5p,hsa-miR-5194, hsa-miR-4505, hsa-miR-4430, hsa-miR-374c-3p, hsa-miR-4506,hsa-miR-4286, hsa-miR-6816-5p, hsa-miR-758-3p, hsa-miR-4535,hsa-miR-490-3p, hsa-miR-6765-5p, hsa-miR-3197, hsa-miR-1271-3p,hsa-miR-92a-1-5p, hsa-miR-8054, hsa-miR-455-5p, hsa-miR-7151-3p,hsa-miR-628-3p, hsa-miR-556-5p, hsa-miR-6726-5p, hsa-miR-1179,hsa-miR-3196, hsa-miR-6858-5p, hsa-miR-6778-5p, hsa-miR-4459,hsa-miR-380-5p, hsa-miR-1273e, hsa-let-7b-3p, hsa-miR-4481,hsa-miR-1908-5p, hsa-miR-149-3p, hsa-miR-651-3p and hsa-miR-124-5p(provided in Table 5), is associated with acute rejection and/or can beused to predict acute rejection in a transplant subject. In a particularembodiment, the miRNAs comprise the three biomarkers hsa-miR-125b-5p,hsa-miR-100-5p and hsa-miR-483-5p.

Predicting Minimization of Immunosuppressive Therapy (IST) Dosage.

In one embodiment, the invention includes a method of detecting asubject that has received an organ transplant and is under IST. Themethod comprises the steps of detecting a level of expression of miRNAindicative of IST in a test sample from the subject, wherein the miRNAis at least one selected from hsa-miR-146b-5p, hsa-miR-424-3p,hsa-miR-125a-5p, hsa-miR-342-3p, hsa-miR-150-5p, hsa-miR-421,hsa-miR-148a-3p, hsa-miR-223-5p, hsa-miR-495-3p, hsa-miR-497-5p,hsa-miR-29a-3p, hsa-miR-30a-5p, hsa-miR-374b-5p, hsa-let-7g-5p,hsa-miR-99a-5p, hsa-miR-18b-5p, hsa-miR-7-1-3p, hsa-miR-181c-5p,hsa-miR-454-3p, hsa-miR-485-3p, hsa-miR-374a-5p, hsa-miR-99b-5p,hsa-miR-192-5p, hsa-miR-191-5p, hsa-miR-21-5p, hsa-miR-24-3p,hsa-miR-27b-3p, hsa-miR-222-3p, hsa-miR-20a-3p and hsa-miR-106b-5p, orany combination thereof. Then comparing the level of expression of themiRNA in the test sample to the level of miRNA in a control sample,wherein an increase between the amount of the miRNA in the test samplerelative to the control sample indicates that the subject is likely tofail a reduction in IST dosage. Further when failure of IST minimizationis indicated, treatment of the subject is recommended.

The invention is based, in part, on the observation that increasedexpression of certain miRNAs comprising hsa-miR-146b-5p, hsa-miR-424-3p,hsa-miR-125a-5p, hsa-miR-342-3p, hsa-miR-150-5p, hsa-miR-421,hsa-miR-148a-3p, hsa-miR-223-5p, hsa-miR-495-3p, hsa-miR-497-5p,hsa-miR-29a-3p, hsa-miR-30a-5p, hsa-miR-374b-5p, hsa-let-7g-5p,hsa-miR-99a-5p, hsa-miR-18b-5p, hsa-miR-7-1-3p, hsa-miR-181c-5p,hsa-miR-454-3p, hsa-miR-485-3p, hsa-miR-374a-5p, hsa-miR-99b-5p,hsa-miR-192-5p, hsa-miR-191-5p, hsa-miR-21-5p, hsa-miR-24-3p,hsa-miR-27b-3p, hsa-miR-222-3p, hsa-miR-20a-3p and hsa-miR-106b-5p(listed in Table 2) is associated with a likelihood of failingminimization of IST dosage in a subject. In a particular embodiments,the miRNAs comprise the three biomarker hsa-miR-146b-5p, hsa-miR-424-3pand hsa-miR-125a-5p.

Based on the data described herein, compositions and methods are nowavailable for the rapid and reliable detection of or prediction of acuterejection even without allograft biopsy, as well as the prediction ofsuccess or failure of minimizing IST dosage.

The amounts of any combinations of the miRNAs listed herein may bedetected according to the methods disclosed herein and compared with acontrol (baseline level). In one embodiment, a difference in the levelof expression of one miRNA indicates that the subject has or isdeveloping acute rejection. However, in alternate embodiments, changesin the amounts of any combination of two, three, four, six, eight, nineor more miRNAs can indicate that the subject has or is developing acuterejection. In this way, the dose of immunosuppression agents can bemodified, e.g., increased or decreased or discontinued and/or new agentscan be added to the administered treatment regimen. In some embodiments,other treatment modalities can be initiated, such as for example,plasmapheresis.

In certain aspects of the present invention, the level of miRNAexpression is determined for one or more miRNA in a sample obtained froma subject. The sample can be a fluid sample such as a blood sample,preferably containing peripheral blood mononuclear cells (PBMCs), aurine sample, preferably containing urinary cells such as epithelialcells, or infiltrating immune cells, a sample of bronchoalveolar lavagefluid, a sample of bile, pleural fluid or peritoneal fluid, or any otherfluid secreted or excreted by a normally or abnormally functioningallograft, or any other fluid resulting from exudation or transudationthrough an allograft or in anatomic proximity to an allograft, or anyfluid that is in physiological contact or proximity with the allograft,or any other body fluid in addition to those recited herein should alsobe considered to be included in the invention.

Any method known to those in the art can be employed for determining thelevel of miRNA expression. For example, a microarray can be used.Microarrays are known in the art and consist of a surface to whichprobes that correspond in sequence to gene products (e.g. mRNAs,polypeptides, fragments thereof etc.) can be specifically hybridized orbound to a known position. To detect at least one miRNA of interest, ahybridization sample is formed by contacting the test sample with atleast one nucleic acid probe. A preferred probe for detecting miRNA is alabeled nucleic acid probe capable of hybridizing to miRNA. The nucleicacid probe can be, for example, a full-length nucleic acid molecule, ora portion thereof, such as an oligonucleotide of at least 10, 15, or 20nucleotides in length and sufficient to specifically hybridize understringent conditions to appropriate miRNA. The hybridization sample ismaintained under conditions which are sufficient to allow specifichybridization of the nucleic acid probe to a miRNA target of interest.Specific hybridization can be performed under high stringency conditionsor moderate stringency conditions, as appropriate. In a preferredembodiment, the hybridization conditions for specific hybridization arehigh stringency. Specific hybridization, if present, is then detectedusing standard methods. If specific hybridization occurs between thenucleic acid probe and a miRNA in the test sample, the sequence that ispresent in the nucleic acid probe is also present in the miRNA of thesubject. More than one nucleic acid probe can also be used.Hybridization intensity data detected by the scanner are automaticallyacquired and processed by the Affymetrix Microarray Suite (MASS)software. Raw data is normalized to expression levels using a targetintensity of 150. An alternate and preferred method to measure miRNAexpression profiles of a small number of different genes is by e.g.either classical TaqMan® Gene Expression Assays or TaqMan® Low DensityArray—micro fluidic cards (Applied Biosystems). Particularly, thisinvention preferably utilizes a microRNA qPCR system. Non-limitingexamples include commercial kits such as the PrimePCRPathways®commercially available from Bio-rad (Berkley, Calif.), the miRCURY LNA™Universal RT microRNA PCR commercially available from Exiqon (Denmark),or the Custom RT2 Profiler PCR Arrays commercially available from Qiagen(Netherlands). Another example of method that can be employed fordetermining the level of miRNA expression is the use of molecularcolor-coded barcodes and single molecule imaging to detect and counthundreds of unique transcripts in a single reaction such as in thenCounter® system from Nanostring Technology® (Seattle, Wash.). Usingthis technology, each color-coded barcode is attached to a singletarget-specific probe corresponding to a gene of interest so that eachcolor-coded barcode represents a single target molecule. Barcodeshybridize directly to the target molecules and can be individuallycounted without the need for amplification providing very sensitivedigital data. After hybridization, the excess probes are removed and theprobe/target complexes are aligned and immobilized in the nCounter®Cartridge. The sample Cartridges are placed in the nCounter® DigitalAnalyzer for data collection and the color codes on the surface of thecartridge are counted and tabulated for each target molecule.

Other technologies contemplated by this invention for profilingmicroRNAs rely on the use of hydrogel particles such as the Firefly™microRNA Assay (Firefly BioWorks Inc, Cambridge, Mass. 02139). Thisassay, based on porous particle, allows target molecules to diffuse andbind in a unique nanoscale three-dimensional scaffold which favorsaccurate multiplexed miRNAs detection in a variety of biologicalsamples. The present invention particularly contemplates the use ofFirefly™ Circulating microRNA Assay for profiling circulating microRNAsbiomarkers directly from a sample such as blood, serum or plasma withoutany prior RNA purification.

The transcriptional state of a sample, particularly miRNAs, may also bemeasured by other nucleic acid expression technologies known in the art.

In one embodiment, the miRNAs are detected in a sample from therecipient of an organ transplant. Any method known to those in the artcan be employed for determining the level of microRNAs (particularly,the miRNAs provided elsewhere herein in Tables 1-5). miRNA can beisolated from the sample using any method known to those in the art.Non-limiting examples include commercial kits, such as the miRNeasy®commercially available from Qiagen (Netherlands) or the Mini Kit the TRIReagent® commercially available from Molecular Research Center, Inc.(Cincinnati, Ohio), can be used to isolate RNA.

Generally, the isolated miRNAs may be amplified using methods known inthe art. Amplification systems utilizing, for example, PCR or RT-PCRmethodologies are known to those skilled in the art. For a generaloverview of amplification technology, see, for example, Dieffenbach etal., PCR Primer: A Laboratory Manual, Cold Spring Harbor LaboratoryPress, New York (1995).

An alternative method for determining the level of microRNAs(particularly, the miRNAs provided elsewhere herein in Tables 1-5)includes the use of molecular beacons and other labeled probes usefulin, for example multiplex PCR. In a multiplex PCR assay, the PCR mixturecontains primers and probes directed to the selected miRNAs PCR product.Typically, a single fluorochrome is used in the assay. The molecularbeacon or probe is detected to determine the level of miRNA. Molecularbeacons are described, for example, by Tyagi and Kramer (NatureBiotechnology 14, 303-308, 1996) and by Andrus and Nichols in U.S.Patent Application Publication No. 20040053284.

Another accurate method for profiling miRNA expression can the use ofNext Generation Sequencing (NGS) including first, second, third as wellas subsequent Next Generations Sequencing technologies. Non limitingexamples could be the nanopore or semiconductor technologies (e.g.Oxford Nanopore Technologies, United Kingdom) or the IlluminamicroRNA-Seq Platform (Luo S., 2012, Methods Mol Biol. 822:183-8).

In some embodiments, upregulation of miRNA level includes increasesabove a baseline level of 1-, 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, 10-, 11-,12-, 13-, 14-, 15-, 16-, 17-, 18-, 19-, 20-fold, or more and any and allpartial integers therebetween; as well as above a baseline level of0.9-, 0.8-, 0.7-, 0.6-, 0.5-, 0.4-, 0.3-, 0.2-, or 0.1-fold, or less.

In some embodiments, the level of expression is determined usinglog-transformed miRNA levels. The log transformation or miRNA levelssubstantially reduce the positive skew in the data. In some embodiments,the level of expression is determined using log-transformed miRNA levelsdetermined by normalizing miRNA levels using a logistic regressionmodel. Logistic regression models are used for prediction of theprobability of occurrence of acute rejection by fitting data to alogistic curve. It is a generalized linear model used for binomialregression.

In some embodiments, for interpretation of quantitative nucleic acidexpression measurements, a normalizer may be needed to correctexpression data for differences in sample input, RNA quality, and RTefficiency between samples. In some embodiments, to accurately assesswhether increased miRNA is significant, the miRNA expression can benormalized to accurately compare levels of expression between samples,e.g., it is a baseline level against which expression is compared. Inquantitative assays, such as for example, quantitative real-time ReverseTranscriptase-PCR (qRT-PCR) normalization can be performed usingspiked-in markers as references against the expression level of a miRNAunder investigation. Normalization includes rendering the measurementsof different arrays or PCR or in particular RT-PCR experimentscomparable by reducing or removing the technical variability. Withinthese experiments there exists a multiplicity of sources capable offalsifying the measurements. Possible technical sources of interferenceare: different efficiency in reverse transcription, labeling orhybridization reactions, as well as problems with the arrays, batcheffects in reagents, or lab-specific conditions. By normalization a morerobust detection of miRNA expression can occur.

Typically, miRNA normalization involves use of spiked-in markers thathave known fractional cycle number or crossing point. These are utilizedas a reference, internal control or reference values in thequantification of miRNA expression. A spiked-in marker exhibits minimumchange of expression and transcription across different miRNA samplesand thus serves as a control, or reference, for the measurement ofvariable miRNA activities across different samples. Spiked-in markerscan be, but are not limited to, UniSp2 and UniSp4 (Exiqon, Denmark).

Receiver Operating Characteristic (ROC) curves can be generated forindividual miRNA levels and a linear combination of miRNA levels todetermine the cutoff points that yielded the highest combinedsensitivity and specificity for detecting ACR or anticipating ACR aswell as detecting the likelihood of successful minimization of IST.

This involves measuring the miRNA levels alone, all together, or in anycombination for the following hsa-miR-125b-5p, hsa-miR-100-5p,hsa-miR-483-5p, hsa-miR-885-5p, hsa-miR-122-5p, hsa-miR-99a-5p,hsa-miR-30a-5p, hsa-miR-497-5p, hsa-miR-194-5p, hsa-miR-34a-5p,hsa-miR-192-5p, hsa-miR-215, hsa-miR-375, hsa-miR-193a-5p,hsa-miR-483-5p, hsa-miR-505-3p, hsa-miR-378a-3p, hsa-miR-193b-3p,hsa-miR-874, hsa-miR-365a-3p, hsa-miR-152, hsa-miR-148a-3p andhsa-miR-29a-5p; and/or hsa-miR-146b-5p, hsa-miR-424-3p, hsa-miR-125a-5p,hsa-miR-342-3p, hsa-miR-150-5p, hsa-miR-421, hsa-miR-148a-3p,hsa-miR-223-5p, hsa-miR-495-3p, hsa-miR-497-5p, hsa-miR-29a-3p,hsa-miR-30a-5p, hsa-miR-374b-5p, hsa-let-7g-5p, hsa-miR-99a-5p,hsa-miR-18b-5p, hsa-miR-7-1-3p, hsa-miR-181c-5p, hsa-miR-454-3p,hsa-miR-485-3p, hsa-miR-374a-5p, hsa-miR-99b-5p, hsa-miR-192-5p,hsa-miR-191-5p, hsa-miR-21-5p, hsa-miR-24-3p, hsa-miR-27b-3p,hsa-miR-222-3p, hsa-miR-20a-3p and hsa-miR-106b-5p. These combinationsare then weighted based on increased expression. These statisticalanalyses with different biomarkers are described in Zhang et al., 2005,Biostatistics Working Paper Series. Other statistical analysis methodsfor quantifying biomarkers known in the art can be used as well.

Compositions

The invention includes a set of preferred probes or primers, eitherlabeled (e.g., fluorescer, quencher, etc.) or unlabeled, that are usefulfor the detection of at least three miRNAs selected from the groupconsisting of hsa-miR-125b-5p, hsa-miR-100-5p, hsa-miR-483-5p,hsa-miR-885-5p, hsa-miR-122-5p, hsa-miR-99a-5p, hsa-miR-30a-5p,hsa-miR-497-5p, hsa-miR-194-5p, hsa-miR-34a-5p, hsa-miR-192-5p,hsa-miR-215, hsa-miR-375, hsa-miR-193a-5p, hsa-miR-483-5p,hsa-miR-505-3p, hsa-miR-378a-3p, hsa-miR-193b-3p, hsa-miR-874,hsa-miR-365a-3p, hsa-miR-152, hsa-miR-148a-3p, hsa-miR-29a-5p (Table1),hsa-miR-4790-5p, hsa-miR-3692-3p, hsa-miR-4433b-3p, hsa-miR-6500-3p,hsa-miR-4445-5p, hsa-miR-5194, hsa-miR-4505, hsa-miR-4430,hsa-miR-374c-3p, hsa-miR-4506, hsa-miR-4286, hsa-miR-6816-5p,hsa-miR-758-3p, hsa-miR-4535, hsa-miR-490-3p, hsa-miR-6765-5p,hsa-miR-3197, hsa-miR-1271-3p, hsa-miR-92a-1-5p, hsa-miR-8054,hsa-miR-455-5p, hsa-miR-7151-3p, hsa-miR-628-3p, hsa-miR-556-5p,hsa-miR-6726-5p, hsa-miR-1179, hsa-miR-3196, hsa-miR-6858-5p,hsa-miR-6778-5p, hsa-miR-4459, hsa-miR-380-5p, hsa-miR-1273e,hsa-let-7b-3p, hsa-miR-4481, hsa-miR-1908-5p, hsa-miR-149-3p,hsa-miR-651-3p and hsa-miR-124-5p (Table 5); and/or hsa-miR-146b-5p,hsa-miR-424-3p, hsa-miR-125a-5p, hsa-miR-342-3p, hsa-miR-150-5p,hsa-miR-421, hsa-miR-148a-3p, hsa-miR-223-5p, hsa-miR-495-3p,hsa-miR-497-5p, hsa-miR-29a-3p, hsa-miR-30a-5p, hsa-miR-374b-5p,hsa-let-7g-5p, hsa-miR-99a-5p, hsa-miR-18b-5p, hsa-miR-7-1-3p,hsa-miR-181c-5p, hsa-miR-454-3p, hsa-miR-485-3p, hsa-miR-374a-5p,hsa-miR-99b-5p, hsa-miR-192-5p, hsa-miR-191-5p, hsa-miR-21-5p,hsa-miR-24-3p, hsa-miR-27b-3p, hsa-miR-222-3p, hsa-miR-20a-3p andhsa-miR-106b-5p (Table 2). Particularly preferred probe sets compriseprobes that are capable of detecting the three biomarkershsa-miR-483-5p, hsa-miR-125b-5p and hsa-miR-100-5p for the diagnosis orprediction of ACR; and the three biomarkers hsa-miR-146b-5p,hsa-miR-424-3p and hsa-miR-125a-5p for the diagnosis of tolerance forminimization of IST.

Kits

In certain embodiments, kits are provided. Commercially available kitsfor use in these methods are, in view of this specification, known tothose of skill in the art. In general, kits will comprise a detectionreagent that is suitable for detecting the presence of a polypeptide ornucleic acid, or mRNA of interest.

In another embodiment, there is a panel of probe sets. Preferred probesets are designed to detect expression of one or more miRNAs and provideinformation about the rejection of a graft and/or the minimization ofIST. Probe sets are particularly useful because they are smaller andcheaper than probe sets that are intended to detect as many miRNAs aspossible in a particular genome. The probe sets are targeted at thedetection of miRNAs that are informative about acute rejection ortolerance for IST minimization. Probe sets may also comprise a large orsmall number of probes that detect miRNAs that are not informative abouttransplant rejection or minimization of IST. Such probes are useful ascontrols and for normalization (e.g., spiked-in markers). Probe sets maybe a dry mixture or a mixture in solution. In some embodiments, probesets can be affixed to a solid substrate to form an array of probes. Itis anticipated that probe sets may also be useful for multiplex PCR. Theprobes may be nucleic acids (e.g., DNA, RNA, chemically modified formsof DNA and RNA), LNAs (Locked nucleic acids), or PNAs (Peptide nucleicacids), or any other polymeric compound capable of specificallyinteracting with the desired nucleic acid sequences.

It is contemplated that kits may be designed for isolating and/ordetecting miRNA in essentially any sample (e.g., urine, blood, etc.),and a wide variety of reagents and methods are, in view of thisspecification, known in the art.

The following examples further illustrate aspects of the presentinvention. However, they are in no way a limitation of the teachings ordisclosure of the present invention as set forth herein.

EXAMPLES

The invention is now described with reference to the following Examples.These Examples are provided for the purpose of illustration only and theinvention should in no way be construed as being limited to theseExamples, but rather should be construed to encompass any and allvariations which become evident as a result of the teaching providedherein.

Without further description, it is believed that one of ordinary skillin the art can, using the preceding description and the followingillustrative examples, make and utilize the compounds of the presentinvention and practice the claimed methods. The following workingexamples therefore, specifically point out the preferred embodiments ofthe present invention, and are not to be construed as limiting in anyway the remainder of the disclosure.

The materials and methods employed in the experiments disclosed hereinare now described.

Materials and Methods Clinical Trial Studies.

Data from three clinical trial studies from The National Institutes ofHealth (NIH) were used herein. The Immune Tolerance Network A-WISH study(ITN study: ITN030, available atwww.clinicaltrials.gov/ct2/show/record/NCT00135694), Clinical Trials inOrgan Transplantation (CTOT-03, available atwww.clinicaltrials.gov/ct2/show/NCT00531921?term=CTOT-03&rank=1), andAdult to Adult Liver Living Donors Liver Transplantation (A2ALL,available atwww.clinicaltrials.gov/ct2/show/NCT02073435?term=A2ALL&rank=2).

Inclusion and Exclusion Criteria.

Study subjects were required to be adult recipients transplanted fornon-immune liver disease, including subjects treated for HCV infectionprior to transplantation who remain SVR at the time of clinicallyindicated liver biopsy. Subjects should receive tacrolimus-basedimmunosuppression. Overall, the CTOT-03 and -04 protocols demonstrateda >95% consent rate among subjects approached for entry intoobservational study involving blood collections.

miRNA Profiles.

RNA was extracted from the subject's serum followed by miRNA profilingand analysis. miRNA profiling was done via miRCURY LNA™ Universal RTmicroRNA PCR panels (Exiqon, Denmark). Serum input is held constant toassure the same eluted volume of nucleic acids is used in each reversetranscription reaction. Spike-in assays controlled the efficiencies ofextraction, reverse transcription and real-time PCR. Standard miRNA PCRarray was used with spiked-in markers that have known Cq (fractionalcycle number or crossing point) value to allow quantitative measurementsof signature miRNAs.

Expression Data Normalization

After Inter-Plate-Calibration (IPC), individual measurements that werebeyond cycle threshold were replaced with the highest Cq value outputfrom the run to allow their inclusion in subsequent processing, as themeasurements were below detection limits rather than true missing value.These values were subtracted from 36 to provide a relative expressionvalue (on log 2-scale) with zero set at approximately the reliablequantification limit for qPCR-based assays. To normalize for technicalprocessing variations, for each sample, the average of the deviationfrom the study population mean for the UniSp2 and UniSp4 (Exiqon,Denmark) exogenous spiked miRNA was subtracted from the relativeexpression values for each miRNA within the respective sample. Fromanalysis of population variance vs. mean plot for the normalized miRNAexpression values, the limit of detection (LOD) was estimated to beabove 0, but could be extended to be above −2, with the limitation ofpopulation standard deviation being below 2.5. Based on this LOD, miRNAthat had a relative expression value below −2 were adjusted to −3. miRNAthat had a population mean below −2 or population standard deviationabove 2.5 were flagged as inappropriate for continuous variableanalysis. If the miRNA had signal above −2 for at least two samples forACR=Yes and ACR=No subgroups, then the miRNA was considered forcategorical analysis after dichotomization of values to ‘1’ if relativeexpression was above −2 or ‘0’ otherwise. miRNA that were not consideredfor continuous variable or categorical analyses were not included insubsequent statistical analyses.

Statistical Analysis

Normalized miRNA expression values were imported into Array Studiosoftware (www.omicsoft.com) for additional data QC and single variableanalysis. Outliers implicated by both Principal Components Analysis(PCA) clustering and MAD scores were excluded. Serum samples collectedduring biopsy-proven ACR and no-rejection episodes were randomlyassigned to discovery (14 ACR and 37 non-ACR samples), and replication(13 ACR and 40 non-ACR samples) sets. The associations between ACRstatus and individual miRNA expression levels were tested using generallinear model, adjusting for the potential cofounder time sincetransplantation. False discovery rate (FDR) was applied for multipletesting correction.

Logistic regression analysis was used to identify parsimonious subsetsof ACR-associated serum miRNAs that discriminated ACR from non-ACRepisodes. Models with up to 6-terms were built with hierarchical forwardwith switching as the variable selection method. From those models inwhich each predictor was significant at P<0.1, provisionally selectionwas based on the miRNA with the greatest log-likelihood and greatestarea under the receiver-operating-characteristic (ROC) curve as thebest-fitting model. The regression estimates from this model defined adiagnostic signature, and area under the curve (AUC), sensitivity, andspecificity were used to evaluate the ability of this signature todiscriminate ACR from non-ACR episodes.

To validate the miRNA diagnostic signature obtained above, theregression coefficients for the miRNAs included in the diagnosticsignature obtained from ITN samples were used to calculate a compositescore to summarize the expression values of these miRNAs for each samplein the replication datasets (samples obtained from CTOT-03 study andsamples collected from non-randomized subjects participating in ITNtrial). The composite scores were then used in a logistic regressionmodel. % correct classification, AUC, sensitivity and specificity werecalculated for these two replication datasets.

To investigate the predictive value of the diagnostic signatureobtained, LOESS (locally estimated scatterplot smoothing) curves withcorresponding 95% confidence intervals (CI) were obtained for theretrospective trajectories of the diagnostic signature, lookingbackwards from the time of biopsy for ACR and non-ACR episodes.

To identify the tolerance biomarkers, 10 serum samples from thosesubjects who were tolerant at the 25% immunosuppression dose werecompared with 11 serum samples from subjects who failed at the 25% ISTdose. The associations between fail-tolerance status and individualmiRNA expression levels at the 25% IST dose were tested using generallinear model, adjusting for the potential cofounder time sincetransplantation. False discovery rate (FDR) was applied for multipletesting correction. Logistic regression analysis was used to build modelconsisting of three IST-tolerance-associated serum miRNAs that passedmultiple-testing correction. The regression estimates from this modeldefined an IST minimization signature, and area under the curve (AUC),sensitivity, and specificity were used to evaluate the ability of thissignature to discriminate subjects who tolerated from subjects whofailed 25% immunosuppression dose at various immunosuppressionminimization dosages.

All statistical analyses were performed in Array Studio software(www.omicsoft.com), NCSS version 8.0.14 (www.ncss.com), and R(cran.r-project.org).

Power and Sample Size.

Power and sample size calculations were performed assuming the use ofgeneral linear model. Estimates from the ITN study were used for powercalculation because of its larger sample size and more stable varianceestimates. Based on the observed within-group variances of the 3-miRNAACR-Dx biomarker model prediction score and the proportion of subjectsexpected to have biopsy proven rejection, 75 subjects (25 rejectioncases and 50 control non-rejection subjects) were estimated in poweranalyses to be needed to have 90% power at alpha=0.00001 to observe asignificant difference in mean at the same magnitude as was observed inthe ITN study, or alpha=0.001 to observe significant difference in meanat 75% level as what observed in the ITN study.

Tables

-   Table 1: List of miRNAs associated with ACR diagnosis with a 2-stage    study.-   Table 2: List of significant miRNAs (P<0.01) between failed and    tolerant samples at 25% immunosuppression minimization.-   Table 3: List of miRNAs associated with ACR diagnosis detected in    the follow up study including 19 ACR and 16 non-ACR samples.-   Table 4: List of top ACR-associated miRNAs identified using Exiqon    human miRNA panel and confirmed with Qiagen Human miRNome miRNA PCR    Array-   Table 5: List of top ACR-associated miRNAs (nominal p<0.15)    identified using Qiagen Human miRNome miRNA PCR Array.-   Table 6: List of the miRNAs biomarkers and their related target    sequence selected for ACR prediction and/or IST minimization    tolerance.-   Table 7: Sequence identifiers for the miRNAs biomarkers and their    related target sequence selected for ACR prediction and/or IST    minimization tolerance.

The results of the experiments are now described in the followingexamples.

Example 1 Identification of Serum miRNA Signatures for the Detection andPrediction of Acute Cellular Rejection (ACR)

The results presented herein demonstrate that the miRNA profilesobtained at the time of clinically indicated biopsy are diagnostic ofbiopsy-confirmed acute rejection at any time after transplantation.

miRNA profiling was performed on 233 serum samples from 42 clinicaltrial participants from the National Institutes of Health ImmuneTolerance in Transplantation-30 (ITN-30) study. This included 33subjects randomized to immunosuppression withdrawal and 9 subjectsrandomized to maintenance, using the miRCURY LNA™ Universal RT microRNAPCR v3 panel (Exiqon, Denmark). The primary aims of this study were: 1)to identify serum miRNA signatures for diagnosis of acute cellularrejection (ACR) events; 2) to identify serum miRNA signatures forprediction of ACR events; 3) to identify serum miRNA signatures todifferentiate subjects who fail immunosuppression withdrawal fromsubjects who develop tolerance; and 4) to identify miRNA markers thatare associated with immunosuppression doses and trough levels. TheExiqon miRNA panel included unique 752 miRNA assays of which 240 wereabove the lower limit of reliable quantification in a sufficientproportion of samples to allow for meaningful statistical analysis. Acomparison of serum samples from biopsy proven rejection and serumsamples without biopsy proven rejection in a two-stage study design (adiscovery phase consisting of 14 ACR samples and 37 non-ACR samples;with a replication phase consisting of 13 ACR samples and 40 non-ACRsamples) was conducted. From the miRNAs that were nominally significant(P<0.05) in the discovery phase 11 of 26 were confirmed at P<0.05 in thereplication phase. In the combined dataset, 15 miRNAs were significantlyassociated with ACR diagnosis after multiple testing correction (FDRadjusted P<0.05) (Table 1). These 15 miRNA include all of the 11 miRNAreplicated between discovery and replication phases. To build a multiplemarker panel/signature that may better differentiate ACR from non-ACR,the aforementioned 15 significant miRNAs and time since randomizationwere used as inputs in logistic regression modeling for forward variableselection. Three miRNAs, hsa-miR-125b, hsa-miR-100 and hsa-miR-483,remained in the final parsimonious model. The logistic regression modelcomposed of these three miRNAs (herein referred to as the 3-miRNA serumACR diagnosis signature) provides ability to differentiate ACR fromnon-ACR with an area under the curve (AUC) of 0.898, 92.6% sensitivityand 84.2% specificity (P=0.0001) (FIG. 1).

Using the aforementioned 3-miRNA serum ACR diagnosis signature as aprediction signature for ACR in liver transplantation, the inventionherein assessed the trajectory towards rejection as well as thediagnosis and prognosis for minimization of the immunosuppressivetherapy (IST) dose. Specifically, the possibility of using the 3-miRNAserum ACR diagnosis signature to predict ACR events was explored beforethe onset of rejection. As shown in the LOESS plot (FIG. 2), the miRNAsignature model score was elevated before the occurrence of ACR (at day0) whereas the level of the signature model score remained un-elevatedin non-ACR group, with the 95% confidence band of ACR separated fromthat of non-ACR 40 days before ACR events.

Table 1 lists the identified miRNAs associated with ACR diagnosis with a2-stage study. The discovery phase with 14 ACR samples and 37 non-ACRsamples identified 26 miRNAs with P<0.05. In replication phase withindependent 13 ACR samples and 40 non-ACR samples, 11 of the 26significant miRNAs were replicated at P<0.05. 25/26 miRNA trended in thesame direction of association, with only one miRNA showing the oppositedirection of association, indicating consistency in both study phases.In the combined dataset, 15 miRNAs were found to be associated with ACRdiagnosis after multiple test correction (FDR P<0.05). The 3 miRNAsutilized to generate the multi-marker signature are marked by the symbol**. The Benjamini-Hochberg procedure (BH step-up procedure) controls thefalse discovery rate (at level alpha) termed FDR-BH.

TABLE 1 Discovery phase Replication Phase Combined (Discovery +Replication) ID Fold-change RawPValue FDR-BH Fold-change RawPValueFDR-BH Fold-change RawPValue FDR-BH hsa-miR-483-5p ** 2.943 0.00090.0325 3.1558 0.0007 0.0174 3.0297 1.5698E−06 0.0002 hsa-miR-885-5p5.0249 9.06E−05 0.0178 3.6042 5.40E−03 0.0341 4.2977 1.76E−06  0.0002hsa-miR-125b-5p ** 2.6807 0.0005 0.0308 2.5825 0.004 0.0341 2.62416.5752E−06 0.0005 hsa-miR-122-5p 4.3631 0.0001 0.0178 2.8355 0.01850.0678 3.5258 0.000013505 0.0008 hsa-miR-99a-5p 3.0213 0.0007 0.03082.1309 0.0173 0.0678 2.5397 0.000032828 0.0014 hsa-miR-30a-5p 2.20020.0048 0.1181 2.0876 0.0025 0.033 2.1308 0.00003452 0.0014hsa-miR-100-5p ** 3.3132 0.0006 0.0308 2.3914 0.0231 0.0678 2.79970.000060205 0.0021 hsa-miR-497-5p 1.8997 0.0117 0.2198 1.7814 0.00660.0341 1.8416 0.0002 0.0053 hsa-miR-194-5p 2.5048 0.0026 0.0702 1.92880.0432 0.102 2.2043 0.0003 0.0083 hsa-miR-34a-5p 2.2139 0.0075 0.15172.224 0.0235 0.0678 2.2307 0.0004 0.0102 hsa-miR-192-5p 2.6448 0.00080.0308 1.7659 0.1278 0.2215 2.1752 0.0008 0.0178 hsa-miR-215 2.88640.0055 0.1218 2.1567 0.0603 0.1206 2.497 0.0009 0.0178 hsa-miR-3752.9171 0.0228 0.2648 2.5796 0.0324 0.0842 2.7311 0.0015 0.0282hsa-miR-193a-5p 2.3295 0.0141 0.2299 1.8149 0.0503 0.1089 2.06 0.00160.0282 hsa-miR-483-3p 3.5557 0.0024 0.0702 1.7078 0.2362 0.2791 2.47120.0029 0.0474 hsa-miR-505-3p 2.0367 0.0161 0.2452 1.4995 0.1608 0.22881.7504 0.0058 0.084 hsa-miR-378a-3p 1.6826 0.0471 0.4419 1.5235 0.10070.1869 1.6069 0.0085 0.0992 hsa-miR-193b-3p 3.0741 0.0192 0.2537 2.07510.176 0.2288 2.5326 0.0088 0.0992 hsa-miR-874 2.4234 0.0243 0.26481.5514 0.1644 0.2288 1.9256 0.0089 0.0992 hsa-miR-365a-3p 2.1253 0.03960.3866 1.6169 0.1482 0.2288 1.8571 0.0108 0.1114 hsa-miR-152 1.81590.025 0.2648 1.3642 0.2 0.2477 1.5698 0.011 0.1114 hsa-miR-148a-3p1.9598 0.0385 0.3866 1.5212 0.1707 0.2288 1.7321 0.0124 0.1206hsa-miR-29a-5p 2.4134 0.0138 0.2299 1.257 0.5467 0.5685 1.7262 0.04310.3758 hsa-miR-210 1.8276 0.0217 0.2648 1.2748 0.5034 0.5453 1.5290.0542 0.4412 hsa-miR-33b-5p 2.2862 0.0175 0.2517 1.114 0.7815 0.78151.6104 0.0652 0.4968 hsa-miR-432-5p −2.288 0.0198 0.2537 1.4459 0.33480.3785 −1.2684 0.3639 0.9992 ** miRNA utilized to generate multi-markersignature (3-miRNA ACR-Dx)

Example 2 Identification of Serum miRNA Signatures for the Prediction ofImmunosuppression Minimization Tolerance

These experiments were designed to identify additional clinicallyrelevant biomarkers for immunosuppression minimization that arepredictive of which subjects will be able to tolerate lower doses ofmedication without inducing rejection. To identify each tolerancebiomarkers, 10 serum samples from those subjects who were tolerant atthe 25% immunosuppression dose (also meant by that a decrease of theinitial IST dose by 75%) were compared with 11 serum samples fromsubjects who failed at the 25% IST dose (these serum samples were taken58 days on average before rejection occurred). The 25% IST dose was usedas the basis for this comparison because a large proportion of subjects(40% of all participants) failed at this stage, and this may representwhere major changes occur in serum miRNAs that best differentiatepatients who may tolerate or fail at lower IST doses As shown in Table2, the level of 30 miRNAs were found to be significantly different atP<0.01 between samples taken from those who eventually failed at the 25%IST dose and those who were tolerant at the 25% IST dose. Three miRNAswere still significant after multiple testing correction (FDR P<0.05).It is noted that the serum miRNA biomarkers for ACR diagnosis are not onthe higher significant list for biomarkers for tolerance, indicatingpotential biological differences in the physiological states ofrejection vs. tolerance.

To test whether the identified tolerance related miRNA biomarkers havepredictive value, a composite score model was constructed including thethree miRNAs that passed the FDR<0.05 significance threshold(hsa-miR-146b, hsa-miR-424 and hsa-miR-125a) for tolerance associationat the 25% IST dose. Composite scores computed based on the expressionlevels of the 3 miRNAs at the 75% IST dose or at randomization (100% ISTdose) were used to test whether those subjects who were tolerant at the25% IST dose could be differentiated from those who failed at the 25%IST dose. As shown in FIG. 3, the 3-miRNA tolerance signature model ateither the 75% (P=0.02) or 100% (P=0.06) IST dose can differentiatethose subject who eventually failed at the 25% IST dose from thosesubject who eventually became tolerance at the 25% IST dose. The scoresfrom the 3-miRNA tolerance signature model at the 75% IST dose wasestimated to have AUC=0.877, sensitivity=0.82, specificity=0.90 topredict who will fail or become tolerant at the 25% IST dose. Theresults demonstrate the ability to greatly improve the IST minimizationprocess by predicting which patients may go on to exhibit a 25% IST doseearly-on during the minimization process when the failure rate isminimal (93% subjects estimated to be able to tolerant a 75% IST dose,based on our ITN data).

Table 2 Lists 30 serum miRNAs that were significant at P<0.01 for thecomparison of failed and tolerant samples at 25% immunosuppressionminimization.

TABLE 2 Failed.vs. Failed.vs.Tolerant Failed.vs.Tolerant Tolerant IDFoldChange Raw P-Value FDR P-Value hsa-miR-146b-5p 3.3138 0.0004 0.044hsa-miR-424-3p 5.9117 0.0004 0.044 hsa-miR-125a-5p 3.6992 0.0006 0.044hsa-miR-342-3p 2.5354 0.0019 0.0719 hsa-miR-150-5p 2.3795 0.002 0.0719hsa-miR-421 6.7364 0.0021 0.0719 hsa-miR-148a-3p 3.133 0.0025 0.0719hsa-miR-223-5p 4.4363 0.0025 0.0719 hsa-miR-495-3p 2.8986 0.0036 0.0719hsa-miR-497-5p 2.6304 0.0039 0.0719 hsa-miR-29a-3p 2.5658 0.0039 0.0719hsa-miR-30a-5p 2.5706 0.0043 0.0719 hsa-miR-374b-5p 4.4668 0.0047 0.0719hsa-let-7g-5p 3.0967 0.0048 0.0719 hsa-miR-99a-5p 2.4666 0.0057 0.0719hsa-miR-18b-5p 2.9925 0.0063 0.0719 hsa-miR-7-1-3p 4.4785 0.0063 0.0719hsa-miR-181c-5p 3.3391 0.0065 0.0719 hsa-miR-454-3p 3.0594 0.0072 0.0719hsa-miR-485-3p 1.8711 0.0073 0.0719 hsa-miR-374a-5p 3.8964 0.0075 0.0719hsa-miR-99b-5p 2.397 0.0079 0.0719 hsa-miR-192-5p 2.606 0.0081 0.0719hsa-miR-191-5p 3.2862 0.0081 0.0719 hsa-miR-21-5p 2.1481 0.0083 0.0719hsa-miR-24-3p 2.7691 0.0083 0.0719 hsa-miR-27b-3p 2.7774 0.0084 0.0719hsa-miR-222-3p 2.2814 0.0086 0.0719 hsa-miR-20a-3p 4.298 0.0087 0.0719hsa-miR-106b-5p 2.8124 0.0094 0.0719

Example 3 Replication of miRNA Sera Signatures for Detection andPrediction of Acute Cellular Rejection (ACR)

To initially replicate the list of 23 ACR-associated miRNAs as well asthe 3-miRNA signature identified previously for ACR diagnosis signature(Phase I study, presented previously in Example 1), miRNA profiling ofserum samples from two independent studies were subsequently analyzedvia the 752 miRCURY LNA™ Universal RT microRNA PCR v3 panels from Exiqon(Denmark). The first study comprised 15 ACR and 5 non-ACR serum samplesfrom the ITN participants who were not randomized to IST withdrawal ormaintenance. The second study comprised 4 ACR and 11 non-ACR samplesfrom the NIH-CTOT03 prospective study. After excluding miRNAs thatfailed standard quality control (QC) measures, 20 of the 23ACR-associated miRNAs were included in logistic regression modeling totest their association with ACR. As shown in Table 3, all 20 miRNAs werereplicated in this follow-up study.

The performance of the previously identified 3-miRNA (hsa-miR-125b,hsa-miR-100 and hsa-miR-483) serum multiple marker signature thatdifferentiates ACR from non-ACR was also evaluated in this follow-upstudy. When using the same coefficients obtained from phase I of thestudy (presented previously in Example 1) in the logistic regressionmodel to derive a composite score composed of the 3-miRNA serum ACRdiagnosis signature, the model also provided excellent ability todifferentiate ACR from non-ACR with AUC of 0.885 (95% CI: 0.94-0.83),with 84% sensitivity and 75% specificity (P=0.01) (FIG. 4). 80% of thesamples were correctly classified using the 3-miRNA serum ACR diagnosissignature.

Table 3 lists 20 replicated miRNAs associated with ACR diagnosisdetected in the follow up study including 19 ACR and 16 non-ACR samples.

TABLE 3 ID Fold-Change Nominal PValue FDR-BH Pvalue hsa-miR-483-3p6.4822 0.0002 0.0034 hsa-miR-122-5p 6.9065 0.001 0.0069 hsa-miR-885-5p6.1095 0.0015 0.0069 hsa-miR-215 4.9237 0.0019 0.0069 hsa-miR-100-5p5.4082 0.002 0.0069 hsa-miR-34a-5p 3.7757 0.0026 0.0069 hsa-miR-1523.9237 0.0027 0.0069 hsa-miR-192-5p 3.645 0.0029 0.0069 hsa-miR-483-5p3.7951 0.0032 0.0069 hsa-miR-148a-3p 3.4924 0.0038 0.0069 hsa-miR-194-5p3.5736 0.0038 0.0069 hsa-miR-365a-3p 3.3761 0.0063 0.0101hsa-miR-193a-5p 2.5899 0.007 0.0101 hsa-miR-125b-5p 3.358 0.0071 0.0101hsa-miR-30a-5p 2.8838 0.0108 0.014 hsa-miR-505-3p 2.2178 0.0112 0.014hsa-miR-378a-3p 2.0742 0.0244 0.0288 hsa-miR-99a-5p 2.8353 0.0322 0.0357hsa-miR-874 2.3653 0.0504 0.0531 hsa-miR-497-5p 2.0478 0.1131 0.1131

Example 4 Additional miRNAs Candidates for Detection and Prediction ofAcute Cellular Rejection (ACR)

To identify additional novel miRNAs that are not included in ExiqonHuman miRNA Ready-to-Use PCR Panels I and II (v.3) (number of miRNAsincluded: 752), Qiagen Human miRNome miRNA PCR Array, which contains amuch broader panel of 2408 human miRNAs, were employed to screen foradditional ACR-associated miRNAs. Fifteen serum samples from the CTOT03study, including four collected at biopsy proven rejection episodes andeleven collected at non-rejection episodes, were used in this screening.As shown in Table 4, a majority of the top ACR-associated miRNAsidentified by the Exiqon platform showed the same direction ofassociation using Qiagen miRNome miRNA PCR array.

Table 4 lists the top ACR-associated miRNA identified using Exiqon humanmiRNA panel that were confirmed with Qiagen Human miRNome miRNA PCRArray

TABLE 4 Exiqon (ITN + CTOT03 Qiagen 130 samples) (CTOT03 15 samples)Nominal Nominal ID Fold Change P-Value Fold Change P-Valuehsa-miR-885-5p 8.8013 6.0689E−15 6.44 0.0318 hsa-miR-122-5p 6.24663.0231E−12 5.67 0.1533 hsa-miR-194-5p 3.8097 9.9819E−12 2.18 0.2486hsa-miR-483-3p 5.4763  1.027E−11 3.63 0.1122 hsa-miR-483-5p 4.07092.6528E−11 4.89 0.0037 hsa-miR-192-5p 3.9011 2.7644E−11 2.1 0.1623hsa-miR-30a-5p 3.1198 1.2351E−10 3.88 0.2258 hsa-miR-193a-5p 3.19711.7252E−09 1.63 0.5229 hsa-miR-378a-3p 2.4777 1.4478E−08 1.55 0.5225hsa-miR-21-5p 2.3019 7.9969E−08 2.77 0.2463 hsa-miR-574-3p 2.574 4.011E−07 5.81 0.0776 hsa-miR-148a-3p 2.6657 6.1793E−07 3.1 0.2199hsa-let-7b-3p 2.6712 2.9049E−06 4.89 0.0123 hsa-miR-365a-3p 2.53820.000010321 1.41 0.5012 hsa-miR-320c 2.292 0.000014479 1.75 0.5081hsa-miR-320b 2.1672 0.000022355 1.37 0.7152 hsa-miR-378a-5p 2.24010.000072482 1.44 0.6297 hsa-miR-1260a 2.0542 0.000073078 3.67 0.1225

Particularly, by using Qiagen Human miRNome miRNA PCR Array, additionalACR-associated miRNAs, that are not included in the Exiqon panels, wereidentified (Table 5). Some of the newly-identified miRNAs showed greaterfold changes between ACR and non-ACR samples than those originallyidentified using Exiqon panels and would potentially provide greaterdiscrimination ability if replicated. Examples of box-and-whisker plotsfor the top five newly-identified miRNAs are shown in FIG. 5.

Table 5 lists the top ACR-associated miRNA (nominal p<0.15) identifiedusing Qiagen Human miRNome miRNA PCR Array.

TABLE 5 15 CTOT03 samples, 4 ACR, 11 non-ACR FDR-BH adjusted ID FoldChange Nominal P-Value P-Value hsa-miR-4790-5p 1840.47 7.21E−05 0.0755hsa-miR-3692-3p 4.27 0.0006 0.2323 hsa-miR-4433b-3p 5.06 0.0009 0.2323hsa-miR-6500-3p −4.51 0.0009 0.2323 hsa-miR-4445-5p 6.78 0.0015 0.2937hsa-miR-5194 6.61 0.002 0.2937 hsa-miR-4505 12.24 0.0024 0.2937hsa-miR-4430 4.26 0.0027 0.2937 hsa-miR-374c-3p 18708.97 0.0028 0.2937hsa-miR-4506 4.4 0.0033 0.2937 hsa-miR-4286 9.75 0.0037 0.2937hsa-miR-483-5p* 4.89 0.0037 0.2937 hsa-miR-6816-5p 39.27 0.0039 0.2937hsa-miR-758-3p* −17.22 0.0039 0.2937 hsa-miR-4535 5.08 0.0047 0.3168hsa-miR-490-3p* −4.22 0.0048 0.3168 hsa-miR-6765-5p 3.12 0.0061 0.3312hsa-miR-3197 6.5 0.0065 0.3312 hsa-miR-1271-3p 4.28 0.0067 0.3312hsa-miR-92a-1-5p* −4.11 0.0068 0.3312 hsa-miR-8054 −8.79 0.007 0.3312hsa-miR-455-5p* −15.06 0.0077 0.3312 hsa-miR-7151-3p 3.67 0.0084 0.3312hsa-miR-628-3p* −5.16 0.0086 0.3312 hsa-miR-556-5p* −8.01 0.0088 0.3312hsa-miR-6726-5p 4.18 0.0088 0.3312 hsa-miR-1179* −8.58 0.0089 0.3312hsa-miR-3196 3.26 0.0094 0.3312 hsa-miR-6858-5p 5.43 0.0099 0.3312hsa-miR-3673 −13.01 0.01 0.3312 hsa-miR-6778-5p 4.81 0.0101 0.3312hsa-miR-4459 3.73 0.0102 0.3312 hsa-miR-380-5p* −25.15 0.0104 0.3312hsa-miR-1273e 2.95 0.0109 0.3354 hsa-let-7b-3p* 4.89 0.0123 0.3683hsa-miR-4481 3.84 0.0132 0.3826 hsa-miR-1908-5p −5.3 0.014 0.3849hsa-miR-149-3p* 4.45 0.0142 0.3849 hsa-miR-651-3p −4.85 0.0147 0.3849hsa-miR-124-5p* −6.76 0.0148 0.3849 *miRNA also included in Exiqon HumanmiRNA Ready-to-Use PCR Panels I and II (v.3)

Example 5 Detailed List of the miRNAs Biomarkers and Their RelatedTarget Sequence Selected for ACR Prediction and/or IST MinimizationTolerance

TABLE 6 microRNA Target Symbol Accession microRNA Sequence (5′-3′)Sequence (5′-3′) hsa-miR-125b-5p MIMAT0000423 ucccugagacccuaacuugugatccctgagaccctaacttgtga hsa-miR-100-5p MIMAT0000098aacccguagauccgaacuugug aacccgtagatccgaacttgtg hsa-miR-483-5pMIMAT0004761 aagacgggaggaaagaagggag aagacgggaggaaagaagggaghsa-miR-885-5p MIMAT0004947 uccauuacacuacccugccucutccattacactaccctgcctct hsa-miR-122-5p MIMAT0000421uggagugugacaaugguguuug tggagtgtgacaatggtgtttg hsa-miR-99a-5pMIMAT0000097 aacccguagauccgaucuugug aacccgtagatccgatcttgtghsa-miR-30a-5p MIMAT0000087 uguaaacauccucgacuggaagtgtaaacatcctcgactggaag hsa-miR-497-5p MIMAT0002820 cagcagcacacugugguuugucagcagcacactgtggtttgt hsa-miR-194-5p MIMAT0000460 uguaacagcaacuccauguggatgtaacagcaactccatgtgga hsa-miR-34a-5p MIMAT0000255uggcagugucuuagcugguugu tggcagtgtcttagctggttgt hsa-miR-192-5pMIMAT0000222 cugaccuaugaauugacagcc ctgacctatgaattgacagcc hsa-miR-215MI0000291 aucauucagaaaugguauacaggaaaaugaccuau atgacctatgaattgacagacgaauugacagacaauauagcugaguuugucuguc auuucuuuaggccaauauucuguaugacugugcuacuucaa hsa-miR-375 MIMAT0000728 uuuguucguucggcucgcgugatttgttcgttcggctcgcgtga hsa-miR-193a-5p MIMAT0004614ugggucuuugcgggcgagauga tgggtctttgcgggcgagatga hsa-miR-483-3pMIMAT0002173 ucacuccucuccucccgucuu tcactcctctcctcccgtctt hsa-miR-505-3pMIMAT0002876 cgucaacacuugcugguuuccu cgtcaacacttgctggtttccthsa-miR-378a-3p MIMAT0000732 acuggacuuggagucagaaggcactggacttggagtcagaagg hsa-miR-193b-3p MIMAT0002819aacuggcccucaaagucccgcu aactggccctcaaagtcccgct hsa-miR-874 MI0005532uuagcccugcggccccacgcaccaggguaagaga ctgccctggcccgagggaccgagacucucgcuuccugcccuggcccgagggaccgacu ggcugggc hsa-miR-365a-3pMIMAT0000710 uaaugccccuaaaaauccuuau taatgcccctaaaaatccttat hsa-miR-152MI0000462 ugucccccccggcccagguucugugauacacucc tcagtgcatgacagaacttgggacucgggcucuggagcagucagugcaugacagaa cuugggcccggaaggacc hsa-miR-148a-3pMIMAT0000243 ucagugcacuacagaacuuugu tcagtgcactacagaactttgthsa-miR-29a-5p MIMAT0004503 acugauuucuuuugguguucagactgatttcttttggtgttcag hsa-miR-146b-5p MIMAT0002809ugagaacugaauuccauaggcu tgagaactgaattccataggct hsa-miR-424-3pMIMAT0004749 caaaacgugaggcgcugcuau caaaacgtgaggcgctgctat hsa-miR-125a-5pMIMAT0000443 ucccugagacccuuuaaccuguga tccctgagaccctttaacctgtgahsa-miR-342-3p MIMAT0000753 ucucacacagaaaucgcacccgutctcacacagaaatcgcacccgt hsa-miR-150-5p MIMAT0000451ucucccaacccuuguaccagug tctcccaacccttgtaccagtg hsa-miR-421 MIMAT0003339aucaacagacauuaauugggcgc atcaacagacattaattgggcgc hsa-miR-223-5pMIMAT0004570 cguguauuugacaagcugaguu cgtgtatttgacaagctgagtthsa-miR-495-3p MIMAT0002817 aaacaaacauggugcacuucuuaaacaaacatggtgcacttctt hsa-miR-29a-3p MIMAT0000086uagcaccaucugaaaucgguua tagcaccatctgaaatcggtta hsa-miR-374b-5pMIMAT0004955 auauaauacaaccugcuaagug atataatacaacctgctaagtg hsa-let-7g-5pMIMAT0000414 ugagguaguaguuuguacaguu tgaggtagtagtttgtacagtthsa-miR-18b-5p MIMAT0001412 uaaggugcaucuagugcaguuagtaaggtgcatctagtgcagttag hsa-miR-7-1-3p MIMAT0004553caacaaaucacagucugccaua caacaaatcacagtctgccata hsa-miR-181c-5pMIMAT0000258 aacauucaaccugucggugagu aacattcaacctgtcggtgagthsa-miR-454-3p MIMAT0003885 uagugcaauauugcuuauagggutagtgcaatattgcttatagggt hsa-miR-485-3p MIMAT0002176gucauacacggcucuccucucu gtcatacacggctctcctctct hsa-miR-374a-5pMIMAT0004688 uuauaauacaaccugauaagug ttataatacaacctgataagtghsa-miR-99b-5p MIMAT0000689 cacccguagaaccgaccuugcgcacccgtagaaccgaccttgcg hsa-miR-191-5p MIMAT0000440caacggaaucccaaaagcagcug caacggaatcccaaaagcagctg hsa-miR-21-5pMIMAT0000076 uagcuuaucagacugauguuga tag cttatcagactgatgttgahsa-miR-24-3p MIMAT0000080 uggcucaguucagcaggaacag tggctcagttcagcaggaacaghsa-miR-27b-3p MIMAT0000419 uucacaguggcuaaguucugc ttcacagtggctaagttctgchsa-miR-222-3p MIMAT0000279 agcuacaucuggcuacugggu agctacatctggctactgggthsa-miR-20a-3p MIMAT0004493 acugcauuaugagcacuuaaagactgcattatgagcacttaaag hsa-miR-106b-5p MIMAT0000680uaaagugcugacagugcagau taaagtgctgacagtgcagat hsa-miR-4790-5p MIMAT0019961aucgcuuuaccauucauguu atcgctttaccattcatgtt hsa-miR-3692-3p MIMAT0018122guuccacacugacacugcagaagu gttccacactgacactgcagaagt hsa-miR-4433b-3pMIMAT0030414 caggaguggggggugggacgu caggagtggggggtgggacgt hsa-miR-6500-3pMIMAT0025455 acacuuguugggaugaccugc acacttgttgggatgacctgc hsa-miR-44455pMIMAT0018963 agauuguuucuuuugccgugca agattgtttcttttgccgtgca hsa-miR-5194MIMAT0021125 ugagggguuuggaaugggaugg tgaggggtttggaatgggatgg hsa-miR-4505MIMAT0019041 aggcugggcugggacgga aggctgggctgggacgga hsa-miR-4430MIMAT0018945 aggcuggagugagcggag aggctggagtgagcggag hsa-miR-374c-3pMIMAT0022735 cacuuagcagguuguauuauau cacttagcaggttgtattatat hsa-miR-4506MIMAT0019042 aaauggguggucugaggcaa aaatgggtggtctgaggcaa hsa-miR-4286MIMAT0016916 accccacuccugguacc accccactcctggtacc hsa-miR-6816-5pMIMAT0027532 uggggcggggcaggucccugc tggggcggggcaggtccctgc hsa-miR-758-3p*MIMAT0003879 uuugugaccugguccacuaacc tttgtgacctggtccactaacc hsa-miR-4535MIMAT0019075 guggaccuggcugggac gtggacctggctgggac hsa-miR-490-3p*MIMAT0002806 caaccuggaggacuccaugcug caacctggaggactccatgctghsa-miR-6765-5p MIMAT0027430 gugaggcggggccaggagggugugugtgaggcggggccaggagggtgtgt hsa-miR-3197 MIMAT0015082ggaggcgcaggcucggaaaggcg ggaggcgcaggctcggaaaggcg hsa-miR-1271-3pMIMAT0022712 agugccugcuaugugccaggca agtgcctgctatgtgccaggcahsa-miR-92a-1-5p* MIMAT0004507 agguugggaucgguugcaaugcuaggttgggatcggttgcaatgct hsa-miR-8054 MIMAT0030981 gaaaguacagaucggaugggugaaagtacagatcggatgggt hsa-miR-455-5p* MIMAT0003150uaugugccuuuggacuacaucg tatgtgcctttggactacatcg hsa-miR-7151-3pMIMAT0028213 cuacaggcuggaaugggcuca ctacaggctggaatgggctca hsa-miR-628-3p*MIMAT0003297 ucuaguaagaguggcagucga tctagtaagagtggcagtcga hsa-miR-556-5p*MIMAT0003220 gaugagcucauuguaauaugag gatgagctcattgtaatatgaghsa-miR-6726-5p MIMAT0027353 cgggagcuggggucugcaggu cgggagctggggtctgcaggthsa-miR-1179* MIMAT0005824 aagcauucuuucauugguugg aagcattctttcattggttgghsa-miR-3196 MIMAT0015080 cggggcggcaggggccuc cggggcggcaggggcctchsa-miR-6858-5p MIMAT0027616 gugaggaggggcuggcagggacgtgaggaggggctggcagggac hsa-miR-6778-5p MIMAT0027456agugggaggacaggaggcaggu agtgggaggacaggaggcaggt hsa-miR-4459 MIMAT0018981ccaggaggcggaggagguggag ccaggaggcggaggaggtggag hsa-miR-380-5p*MIMAT0000734 ugguugaccauagaacaugcgc tggttgaccatagaacatgcgc hsa-miR-1273eMIMAT0018079 uugcuugaacccaggaagugga ttgcttgaacccaggaagtggahsa-let-7b-3p* MIMAT0004482 cuauacaaccuacugccuucccctatacaacctactgccttccc hsa-miR-4481 MIMAT0019015 ggagugggcuggugguuggagtgggctggtggtt hsa-miR-1908-5p MIMAT0007881 cggcggggacggcgauugguccggcggggacggcgattggtc hsa-miR-149-3p* MIMAT0004609 agggagggacgggggcugugcagggagggacgggggctgtgc hsa-miR-651-3p MIMAT0026624 aaaggaaaguguauccuaaaagaaaggaaagtgtatcctaaaag hsa-miR-124-5p* MIMAT0004591cguguucacagcggaccuugau cgtgttcacagcggaccttgatThe microRNAs' nucleotides sequences in “bold” correspond to the onesthat do not have a matching target sequence (i.e. probe) listed in thetable 6.

-   *miRNA also included in Exiqon Human miRNA Ready-to-Use PCR Panels I    and II (v.3)

Table 7 lists the sequence identifiers for the miRNAs biomarkers andtheir related target sequence (listed in Table 6) selected for ACRprediction and/or IST minimization tolerance.

TABLE 7 SEQ. ID for miRNA Symbol SEQ. ID for miRNA sequence Targetsequence hsa-miR-125b-5p SEQ ID NO: 1 SEQ ID NO: 49 hsa-miR-100-5p SEQID NO: 2 SEQ ID NO: 50 hsa-miR-483-5p SEQ ID NO: 3 SEQ ID NO: 51hsa-miR-885-5p SEQ ID NO: 4 SEQ ID NO: 52 hsa-miR-122-5p SEQ ID NO: 5SEQ ID NO: 53 hsa-miR-99a-5p SEQ ID NO: 6 SEQ ID NO: 54 hsa-miR-30a-5pSEQ ID NO: 7 SEQ ID NO: 55 hsa-miR-497-5p SEQ ID NO: 8 SEQ ID NO: 56hsa-miR-194-5p SEQ ID NO: 9 SEQ ID NO: 57 hsa-miR-34a-5p SEQ ID NO: 10SEQ ID NO: 58 hsa-miR-192-5p SEQ ID NO: 11 SEQ ID NO: 59 hsa-miR-215 SEQID NO: 12 SEQ ID NO: 60 hsa-miR-375 SEQ ID NO: 13 SEQ ID NO: 61hsa-miR-193a-5p SEQ ID NO: 14 SEQ ID NO: 62 hsa-miR-483-3p SEQ ID NO: 15SEQ ID NO: 63 hsa-miR-505-3p SEQ ID NO: 16 SEQ ID NO: 64 hsa-miR-378a-3pSEQ ID NO: 17 SEQ ID NO: 65 hsa-miR-193b-3p SEQ ID NO: 18 SEQ ID NO: 66hsa-miR-874 SEQ ID NO: 19 SEQ ID NO: 67 hsa-miR-365a-3p SEQ ID NO: 20SEQ ID NO: 68 hsa-miR-152 SEQ ID NO: 21 SEQ ID NO: 69 hsa-miR-148a-3pSEQ ID NO: 22 SEQ ID NO: 70 hsa-miR-29a-5p SEQ ID NO: 23 SEQ ID NO: 71hsa-miR-146b-5p SEQ ID NO: 24 SEQ ID NO: 72 hsa-miR-424-3p SEQ ID NO: 25SEQ ID NO: 73 hsa-miR-125a-5p SEQ ID NO: 26 SEQ ID NO: 74 hsa-miR-342-3pSEQ ID NO: 27 SEQ ID NO: 75 hsa-miR-150-5p SEQ ID NO: 28 SEQ ID NO: 76hsa-miR-421 SEQ ID NO: 29 SEQ ID NO: 77 hsa-miR-223-5p SEQ ID NO: 30 SEQID NO: 78 hsa-miR-495-3p SEQ ID NO: 31 SEQ ID NO: 79 hsa-miR-29a-3p SEQID NO: 32 SEQ ID NO: 80 hsa-miR-374b-5p SEQ ID NO: 33 SEQ ID NO: 81hsa-let-7g-5p SEQ ID NO: 34 SEQ ID NO: 82 hsa-miR-18b-5p SEQ ID NO: 35SEQ ID NO: 83 hsa-miR-7-1-3p SEQ ID NO: 36 SEQ ID NO: 84 hsa-miR-181c-5pSEQ ID NO: 37 SEQ ID NO: 85 hsa-miR-454-3p SEQ ID NO: 38 SEQ ID NO: 86hsa-miR-485-3p SEQ ID NO: 39 SEQ ID NO: 87 hsa-miR-374a-5p SEQ ID NO: 40SEQ ID NO: 88 hsa-miR-99b-5p SEQ ID NO: 41 SEQ ID NO: 89 hsa-miR-191-5pSEQ ID NO: 42 SEQ ID NO: 90 hsa-miR-21-5p SEQ ID NO: 43 SEQ ID NO: 91hsa-miR-24-3p SEQ ID NO: 44 SEQ ID NO: 92 hsa-miR-27b-3p SEQ ID NO: 45SEQ ID NO: 93 hsa-miR-222-3p SEQ ID NO: 46 SEQ ID NO: 94 hsa-miR-20a-3pSEQ ID NO: 47 SEQ ID NO: 95 hsa-miR-106b-5p SEQ ID NO: 48 SEQ ID NO: 96hsa-miR-4790-5p SEQ ID NO: 97 SEQ ID NO: 135 hsa-miR-3692-3p SEQ ID NO:98 SEQ ID NO: 136 hsa-miR-4433b-3p SEQ ID NO: 99 SEQ ID NO: 137hsa-miR-6500-3p SEQ ID NO: 100 SEQ ID NO: 138 hsa-miR-4445-5p SEQ ID NO:101 SEQ ID NO: 139 hsa-miR-5194 SEQ ID NO: 102 SEQ ID NO: 140hsa-miR-4505 SEQ ID NO: 103 SEQ ID NO: 141 hsa-miR-4430 SEQ ID NO: 104SEQ ID NO: 142 hsa-miR-374c-3p SEQ ID NO: 105 SEQ ID NO: 143hsa-miR-4506 SEQ ID NO: 106 SEQ ID NO: 144 hsa-miR-4286 SEQ ID NO: 107SEQ ID NO: 145 hsa-miR-6816-5p SEQ ID NO: 108 SEQ ID NO: 146hsa-miR-758-3p* SEQ ID NO: 109 SEQ ID NO: 147 hsa-miR-4535 SEQ ID NO:110 SEQ ID NO: 148 hsa-miR-490-3p* SEQ ID NO: 111 SEQ ID NO: 149hsa-miR-6765-5p SEQ ID NO: 112 SEQ ID NO: 150 hsa-miR-3197 SEQ ID NO:113 SEQ ID NO: 151 hsa-miR-1271-3p SEQ ID NO: 114 SEQ ID NO: 152hsa-miR-92a-1-5p* SEQ ID NO: 115 SEQ ID NO: 153 hsa-miR-8054 SEQ ID NO:116 SEQ ID NO: 154 hsa-miR-455-5p* SEQ ID NO: 117 SEQ ID NO: 155hsa-miR-7151-3p SEQ ID NO: 118 SEQ ID NO: 156 hsa-miR-628-3p* SEQ ID NO:119 SEQ ID NO: 157 hsa-miR-556-5p* SEQ ID NO: 120 SEQ ID NO: 158hsa-miR-6726-5p SEQ ID NO: 121 SEQ ID NO: 159 hsa-miR-1179* SEQ ID NO:122 SEQ ID NO: 160 hsa-miR-3196 SEQ ID NO: 123 SEQ ID NO: 161hsa-miR-6858-5p SEQ ID NO: 124 SEQ ID NO: 162 hsa-miR-6778-5p SEQ ID NO:125 SEQ ID NO: 163 hsa-miR-4459 SEQ ID NO: 126 SEQ ID NO: 164hsa-miR-380-5p* SEQ ID NO: 127 SEQ ID NO: 165 hsa-miR-1273e SEQ ID NO:128 SEQ ID NO: 166 hsa-let-7b-3p* SEQ ID NO: 129 SEQ ID NO: 167hsa-miR-4481 SEQ ID NO: 130 SEQ ID NO: 168 hsa-miR-1908-5p SEQ ID NO:131 SEQ ID NO: 169 hsa-miR-149-3p* SEQ ID NO: 132 SEQ ID NO: 170hsa-miR-651-3p SEQ ID NO: 133 SEQ ID NO: 171 hsa-miR-124-5p* SEQ ID NO:134 SEQ ID NO: 172

The disclosures of each and every patent, patent application, andpublication cited herein are hereby incorporated herein by reference intheir entirety.

While the present invention has been disclosed with reference tospecific embodiments, it is apparent that other embodiments andvariations of the present invention may be devised by others skilled inthe art without departing from the true spirit and scope of theinvention. The appended claims are intended to be construed to includeall such embodiments and equivalent variations.

What is claimed is:
 1. A method for detecting or predicting transplantrejection of a transplanted organ in a subject, the method comprising:i. determining a level of at least one miRNA expression in a sample fromthe subject; ii. comparing the level of at least one miRNA in the samplefrom the subject relative to a baseline level in a control wherein adifference in the level of the least one miRNA in the sample from thelevel of the at least one miRNA in the control is indicative of an acutetransplant rejection; and, iii. wherein when acute transplant rejectionis indicated, treatment for the rejection is recommended.
 2. The methodof claim 1, wherein the acute transplant rejection comprises acutecellular rejection (ACR).
 3. The method of claim 1, wherein the at leastone miRNA is selected from the group consisting of SEQ ID NOs: 1-3. 4.The method of claim 1, wherein the at least one miRNA is selected fromthe group consisting of SEQ ID NOs: 4-15.
 5. The method of claim 1,wherein the at least one miRNA is selected from the group consisting SEQID NOs: 16-23.
 6. The method of claim 1, wherein the at least one miRNAis selected from the group consisting of SEQ ID NOs: 1-23.
 7. The methodof claim 1, wherein the at least one miRNA is selected from the groupconsisting of SEQ ID NOs: 1-23 and 97-134.
 8. The method of claim 1,wherein the subject is a mammal.
 9. The method of claim 8, wherein themammal is a human.
 10. The method of claim 1, wherein the level of theat least one miRNA is higher than the level of the at least one miRNA inthe control by at least 1 fold.
 11. The method of claim 1, whereindetermining the level of the at least one miRNA employs at least onetechnique selected from the group consisting of reverse transcription,PCR, microarray, and Next Generation Sequencing.
 12. The method of claim1, wherein the sample is at least one selected from the group consistingof urine, peripheral blood, serum, bile, bronchoalveolar lavage (BAL)fluid, pericardial fluid, gastrointestinal fluids, stool samples,biological fluid gathered from an anatomic area in proximity to anallograft, and biological fluid from an allograft.
 13. The method ofclaim 1, wherein the transplanted organ is at least one selected fromthe group consisting of heart, liver, lung, kidney, an intestine,pancreas, pancreatic islet cells, eye, skin, and stem cells.
 14. Themethod of claim 1, wherein the comparison of level of miRNA expressionis computed in a regression model to indicate a trajectory of acuterejection of the transplanted organ.
 15. A method for predictingminimization of immunosuppression therapy (IST) in a transplant subject,the method comprising: i. determining a level of at least one miRNAexpression in a sample from the subject; ii. comparing the level of atleast one miRNA in the sample from the subject relative to a baselinelevel in a control wherein a difference in the level of the least onemiRNA in the sample from the level of the at least one miRNA in thecontrol is indicative of likelihood of success or failure of ISTminimization; and, iii. wherein when failure of IST minimization isindicated, treatment of the subject is recommended.
 16. The method ofclaim 15, wherein the at least one miRNA is selected from the groupconsisting of SEQ ID NOs: 24-26.
 17. The method of claim 15, wherein theat least one miRNA is selected from the group consisting of SEQ ID NOs:6-8, 22, 27-48.
 18. The method of claim 15, wherein the at least onemiRNA is selected from the group consisting of SEQ ID NOs: 6-8, 22,24-48.
 19. The method of claim 15, wherein the minimization of IST islower than the initial dosage by at least 75%.
 20. The method of claim15, wherein the minimization of IST is lower than the initial dosage byat least 25%, by at least 30%, by at least 35%, by at least 40%, by atleast 45%, by at least 50%, by at least 55%, by at least 60%, by atleast 65%, by at least 70%, by at least 75%, by at least 80%, by atleast 85%, by at least 90%, by at least 95%, or by at least 100%. 21.The method of claim 15, wherein the subject is a mammal.
 22. The methodof claim 21, wherein the mammal is a human.
 23. The method of claim 15,wherein the level of the at least one miRNA is higher than the level ofthe at least one miRNA in the control by at least 1 fold.
 24. The methodof claim 15, wherein determining the level of the at least one microRNAutilizes at least one technique selected from the group consisting ofreverse transcription, PCR, microarray, Next Generation Sequencing. 25.The method of claim 15, wherein the sample is at least one selected fromthe group consisting of urine, peripheral blood, serum, bile,bronchoalveolar lavage (BAL) fluid, pericardial fluid, gastrointestinalfluids, stool samples, biological fluid gathered from an anatomic areain proximity to an allograft, and biological fluid from an allograft.26. The method of claim 15, wherein the transplanted organ is at leastone selected from the group consisting of heart, liver, lung, kidney, anintestine, pancreas, pancreatic islet cells, eye, skin, and stem cells.27. The method of claim 15, wherein the comparison of level of miRNAexpression is computed in a regression model to predict the likelihoodof success or failure of IST minimization.
 28. A composition fordetecting or predicting transplant rejection of a transplanted organ ina subject comprising a plurality of miRNAs consisting of SEQ ID NOs:1-23.
 29. A composition for detecting or predicting transplant rejectionof a transplanted organ in a subject comprising a plurality of miRNAsconsisting of SEQ ID NOs: 1-23 and 97-134.
 30. A kit comprising: aplurality of oligonucleotides that are configured to detect at least onemiRNA from selected from the group consisting of SEQ ID NOs: 1-23 and97-134.
 31. The kit of claim 30, wherein the oligonucleotides areconfigured to detect at least SEQ ID NOs: 1-3.
 32. The kit of claim 30,wherein at least one of the oligonucleotides is selected from the groupconsisting of SEQ ID NOs: 49-71 and 135-172.
 33. A composition fordetecting or predicting the ability, or non-ability, of minimizing ISTdosage in a subject post-transplantation comprising a plurality ofmiRNAs consisting of SEQ ID NOs: 6-8, 22, 24-48.
 34. A kit comprising: aplurality of oligonucleotides that are configured to detect at least onemiRNA from the group consisting of SEQ ID NOs: 6-8, 22, 24-48.
 35. Thekit of claim 34, wherein the oligonucleotides are configured to detectat least SEQ ID NOs: 24-26.
 36. The kit of claim 34, wherein at leastone of the oligonucleotides is selected from the group consisting of SEQID NOs: 53-55, 70, 72-96.